idx int64 1 56k | question stringlengths 15 155 | answer stringlengths 2 29.2k ⌀ | question_cut stringlengths 15 100 | answer_cut stringlengths 2 200 ⌀ | conversation stringlengths 47 29.3k | conversation_cut stringlengths 47 301 |
|---|---|---|---|---|---|---|
40,101 | What is coskewness and how can it be calculated? | This link might help you get a clearer idea: http://www.quantatrisk.com/2013/01/20/coskewness-and-cokurtosis/
It includes the mathematical definition along with a Matlab implementation. | What is coskewness and how can it be calculated? | This link might help you get a clearer idea: http://www.quantatrisk.com/2013/01/20/coskewness-and-cokurtosis/
It includes the mathematical definition along with a Matlab implementation. | What is coskewness and how can it be calculated?
This link might help you get a clearer idea: http://www.quantatrisk.com/2013/01/20/coskewness-and-cokurtosis/
It includes the mathematical definition along with a Matlab implementation. | What is coskewness and how can it be calculated?
This link might help you get a clearer idea: http://www.quantatrisk.com/2013/01/20/coskewness-and-cokurtosis/
It includes the mathematical definition along with a Matlab implementation. |
40,102 | Covariate shift correction: standard implementation in R? | Take a look at the twang package on CRAN. It casts the problem in terms of observational studies and propensity scoring, but the application is the same. Read the primary academic article for many more details and discussion: "Propensity Score Estimation with Boosted Regression for Evaluating Adolescent Substance Abu... | Covariate shift correction: standard implementation in R? | Take a look at the twang package on CRAN. It casts the problem in terms of observational studies and propensity scoring, but the application is the same. Read the primary academic article for many m | Covariate shift correction: standard implementation in R?
Take a look at the twang package on CRAN. It casts the problem in terms of observational studies and propensity scoring, but the application is the same. Read the primary academic article for many more details and discussion: "Propensity Score Estimation with ... | Covariate shift correction: standard implementation in R?
Take a look at the twang package on CRAN. It casts the problem in terms of observational studies and propensity scoring, but the application is the same. Read the primary academic article for many m |
40,103 | Covariate shift correction: standard implementation in R? | I don't know of any packaged functions to perform correction for co-variate shift, but here is something that might work.
You could build a random-forest classifier between the training and test set (to estimate test-set probabilities). You can then learn a random-forest on the training set by sampling according to th... | Covariate shift correction: standard implementation in R? | I don't know of any packaged functions to perform correction for co-variate shift, but here is something that might work.
You could build a random-forest classifier between the training and test set | Covariate shift correction: standard implementation in R?
I don't know of any packaged functions to perform correction for co-variate shift, but here is something that might work.
You could build a random-forest classifier between the training and test set (to estimate test-set probabilities). You can then learn a ran... | Covariate shift correction: standard implementation in R?
I don't know of any packaged functions to perform correction for co-variate shift, but here is something that might work.
You could build a random-forest classifier between the training and test set |
40,104 | Non-intuitive answer from a Poisson regression | A brief (actually it turned out quite lengthy) update on material in the comments (as covered by @cardinal and myself above):
I think the main problems are arising from not specifying an offset variable (as per my comment above, see StasK's note on Interpretation of Coefficient answer) which would usually be the natura... | Non-intuitive answer from a Poisson regression | A brief (actually it turned out quite lengthy) update on material in the comments (as covered by @cardinal and myself above):
I think the main problems are arising from not specifying an offset variab | Non-intuitive answer from a Poisson regression
A brief (actually it turned out quite lengthy) update on material in the comments (as covered by @cardinal and myself above):
I think the main problems are arising from not specifying an offset variable (as per my comment above, see StasK's note on Interpretation of Coeffi... | Non-intuitive answer from a Poisson regression
A brief (actually it turned out quite lengthy) update on material in the comments (as covered by @cardinal and myself above):
I think the main problems are arising from not specifying an offset variab |
40,105 | Where can I find time series data to assess accuracy of forecast? [closed] | The International Institute of Forecasters has some datasets (under "Resources"). One very commonly used dataset is the one from the M3 competition.
The Forecasting Principles website (affiliated with the IIF) has some datasets.
Here is the Time Series Data Library created by Rob Hyndman, which @IrishStat mentioned.
Yo... | Where can I find time series data to assess accuracy of forecast? [closed] | The International Institute of Forecasters has some datasets (under "Resources"). One very commonly used dataset is the one from the M3 competition.
The Forecasting Principles website (affiliated with | Where can I find time series data to assess accuracy of forecast? [closed]
The International Institute of Forecasters has some datasets (under "Resources"). One very commonly used dataset is the one from the M3 competition.
The Forecasting Principles website (affiliated with the IIF) has some datasets.
Here is the Time... | Where can I find time series data to assess accuracy of forecast? [closed]
The International Institute of Forecasters has some datasets (under "Resources"). One very commonly used dataset is the one from the M3 competition.
The Forecasting Principles website (affiliated with |
40,106 | Precision and recall in a multi-class classification system? | For those who might be interested in an answer, this comes from a much more knowledgeable source than me (PhD candidate in NLP):
When doing multiclass classification, precision and recall are really
only properly defined for individual classes (you can average across
classes to get a general scores for the entir... | Precision and recall in a multi-class classification system? | For those who might be interested in an answer, this comes from a much more knowledgeable source than me (PhD candidate in NLP):
When doing multiclass classification, precision and recall are really
| Precision and recall in a multi-class classification system?
For those who might be interested in an answer, this comes from a much more knowledgeable source than me (PhD candidate in NLP):
When doing multiclass classification, precision and recall are really
only properly defined for individual classes (you can av... | Precision and recall in a multi-class classification system?
For those who might be interested in an answer, this comes from a much more knowledgeable source than me (PhD candidate in NLP):
When doing multiclass classification, precision and recall are really
|
40,107 | Factor analysis on multiply imputed data | Factor analysis or principal components analysis may indeed yield solutions whose answers are rotated or mirrored versions of each other, so averaging the person scores is not a good idea. Several authors have explored the use of Procrustes analysis to correct for the rotational indetermination, so try searching on 'mu... | Factor analysis on multiply imputed data | Factor analysis or principal components analysis may indeed yield solutions whose answers are rotated or mirrored versions of each other, so averaging the person scores is not a good idea. Several aut | Factor analysis on multiply imputed data
Factor analysis or principal components analysis may indeed yield solutions whose answers are rotated or mirrored versions of each other, so averaging the person scores is not a good idea. Several authors have explored the use of Procrustes analysis to correct for the rotational... | Factor analysis on multiply imputed data
Factor analysis or principal components analysis may indeed yield solutions whose answers are rotated or mirrored versions of each other, so averaging the person scores is not a good idea. Several aut |
40,108 | Factor analysis on multiply imputed data | Do confirmatory factor analysis instead, which would help fixing the loadings to have the same sign in all the imputations. At its heart, EFA is exploratory, while MI framework is that of parameter estimation. They just don't go together well: MI is not particularly suitable either for testing or exploratory analysis. ... | Factor analysis on multiply imputed data | Do confirmatory factor analysis instead, which would help fixing the loadings to have the same sign in all the imputations. At its heart, EFA is exploratory, while MI framework is that of parameter es | Factor analysis on multiply imputed data
Do confirmatory factor analysis instead, which would help fixing the loadings to have the same sign in all the imputations. At its heart, EFA is exploratory, while MI framework is that of parameter estimation. They just don't go together well: MI is not particularly suitable eit... | Factor analysis on multiply imputed data
Do confirmatory factor analysis instead, which would help fixing the loadings to have the same sign in all the imputations. At its heart, EFA is exploratory, while MI framework is that of parameter es |
40,109 | Elementary approach to higher order asymptotics | The higher order asymptotics books that I have on my shelf are Barndord-Nielsen and Cox, Brazzale, Davison and Reid, and Young and Smith (the very latter was my dissertation adviser, and I think he has a great ability to explain very complex concepts in a reasonably understandable way; his review of non-standard proble... | Elementary approach to higher order asymptotics | The higher order asymptotics books that I have on my shelf are Barndord-Nielsen and Cox, Brazzale, Davison and Reid, and Young and Smith (the very latter was my dissertation adviser, and I think he ha | Elementary approach to higher order asymptotics
The higher order asymptotics books that I have on my shelf are Barndord-Nielsen and Cox, Brazzale, Davison and Reid, and Young and Smith (the very latter was my dissertation adviser, and I think he has a great ability to explain very complex concepts in a reasonably under... | Elementary approach to higher order asymptotics
The higher order asymptotics books that I have on my shelf are Barndord-Nielsen and Cox, Brazzale, Davison and Reid, and Young and Smith (the very latter was my dissertation adviser, and I think he ha |
40,110 | Elementary approach to higher order asymptotics | In addition to the other answer, a simpler book to read about this (and newer) is maybe Ronald Butler: "Saddlepoint approximations with applications". I guess it would be difficult to find a more elementary treatment than this!
As others mentioned Edgeworth approximations: Saddlepoint approximations can be seen as a ... | Elementary approach to higher order asymptotics | In addition to the other answer, a simpler book to read about this (and newer) is maybe Ronald Butler: "Saddlepoint approximations with applications". I guess it would be difficult to find a more ele | Elementary approach to higher order asymptotics
In addition to the other answer, a simpler book to read about this (and newer) is maybe Ronald Butler: "Saddlepoint approximations with applications". I guess it would be difficult to find a more elementary treatment than this!
As others mentioned Edgeworth approximation... | Elementary approach to higher order asymptotics
In addition to the other answer, a simpler book to read about this (and newer) is maybe Ronald Butler: "Saddlepoint approximations with applications". I guess it would be difficult to find a more ele |
40,111 | Understanding the predictive distribution in gaussian linear regression | The Posterior predictive distribution is a weighted average over your hypothesis space where each hypothesis is weighted by it's posterior probability. In Bayesian analysis, beliefs are expressed as entire distributions rather than point estimates. In your example, you have a posterior distribution over all possible we... | Understanding the predictive distribution in gaussian linear regression | The Posterior predictive distribution is a weighted average over your hypothesis space where each hypothesis is weighted by it's posterior probability. In Bayesian analysis, beliefs are expressed as e | Understanding the predictive distribution in gaussian linear regression
The Posterior predictive distribution is a weighted average over your hypothesis space where each hypothesis is weighted by it's posterior probability. In Bayesian analysis, beliefs are expressed as entire distributions rather than point estimates.... | Understanding the predictive distribution in gaussian linear regression
The Posterior predictive distribution is a weighted average over your hypothesis space where each hypothesis is weighted by it's posterior probability. In Bayesian analysis, beliefs are expressed as e |
40,112 | Understanding the predictive distribution in gaussian linear regression | The proof is simple. Ignore the Integral for a bit and check the definition of the Expectation (mean) and Covariance (Variance):
$f_{*}=x_{*}^Tw \implies \mathbb{E}[f_{*}]=x_{*}^T\mathbb{E}[w] \implies \mu_{f_{*}}=x_{*}^T\bar{w}=\sigma_n x_{*}^TA^{-1}Xy$
In the same sense: $\mathbb{E}[f_{*}f_{*}^T]=\mathbb{E}[x_{*}^Tw... | Understanding the predictive distribution in gaussian linear regression | The proof is simple. Ignore the Integral for a bit and check the definition of the Expectation (mean) and Covariance (Variance):
$f_{*}=x_{*}^Tw \implies \mathbb{E}[f_{*}]=x_{*}^T\mathbb{E}[w] \impli | Understanding the predictive distribution in gaussian linear regression
The proof is simple. Ignore the Integral for a bit and check the definition of the Expectation (mean) and Covariance (Variance):
$f_{*}=x_{*}^Tw \implies \mathbb{E}[f_{*}]=x_{*}^T\mathbb{E}[w] \implies \mu_{f_{*}}=x_{*}^T\bar{w}=\sigma_n x_{*}^TA^... | Understanding the predictive distribution in gaussian linear regression
The proof is simple. Ignore the Integral for a bit and check the definition of the Expectation (mean) and Covariance (Variance):
$f_{*}=x_{*}^Tw \implies \mathbb{E}[f_{*}]=x_{*}^T\mathbb{E}[w] \impli |
40,113 | Understanding the predictive distribution in gaussian linear regression | This is my understanding which did not follow the thought of marginalization. Hope it is correct.
$f_* = x_*^Tw$ (This is equation 2.1 in the book)
Therefore,
$p(f_*|x_*,X,y) = p(x_*^Tw|x_*,X,y) = x_*^Tp(w|x_*,X,y) = x_*^Tp(w|X,y)$
(remember that $w$ is independent of $x_*$)
Therefore,
$p(f_*|x_*,X,y)= x_*^Tp(w|X,y)$... | Understanding the predictive distribution in gaussian linear regression | This is my understanding which did not follow the thought of marginalization. Hope it is correct.
$f_* = x_*^Tw$ (This is equation 2.1 in the book)
Therefore,
$p(f_*|x_*,X,y) = p(x_*^Tw|x_*,X,y) = x_ | Understanding the predictive distribution in gaussian linear regression
This is my understanding which did not follow the thought of marginalization. Hope it is correct.
$f_* = x_*^Tw$ (This is equation 2.1 in the book)
Therefore,
$p(f_*|x_*,X,y) = p(x_*^Tw|x_*,X,y) = x_*^Tp(w|x_*,X,y) = x_*^Tp(w|X,y)$
(remember that... | Understanding the predictive distribution in gaussian linear regression
This is my understanding which did not follow the thought of marginalization. Hope it is correct.
$f_* = x_*^Tw$ (This is equation 2.1 in the book)
Therefore,
$p(f_*|x_*,X,y) = p(x_*^Tw|x_*,X,y) = x_ |
40,114 | Understanding the predictive distribution in gaussian linear regression | The general idea of the "prediction distribution" has been expressed in @jerad's answer. In short, Bayesians regard everything as random, hence seeking for their distribution which preserves all the information. (Of course you can always derive a point estimator from it at the last step, for example by maximum vote pri... | Understanding the predictive distribution in gaussian linear regression | The general idea of the "prediction distribution" has been expressed in @jerad's answer. In short, Bayesians regard everything as random, hence seeking for their distribution which preserves all the i | Understanding the predictive distribution in gaussian linear regression
The general idea of the "prediction distribution" has been expressed in @jerad's answer. In short, Bayesians regard everything as random, hence seeking for their distribution which preserves all the information. (Of course you can always derive a p... | Understanding the predictive distribution in gaussian linear regression
The general idea of the "prediction distribution" has been expressed in @jerad's answer. In short, Bayesians regard everything as random, hence seeking for their distribution which preserves all the i |
40,115 | Covariance of INID order statistics | It is known. We can even make a more precise and slightly stronger statement.
The following demonstration rests on two ideas. The first is that for any random variable $X$, the conditional expectation
$$\mathbb{E}[X | X \le t]$$
is a non-decreasing function of $t$. This is geometrically obvious: as $t$ increases, th... | Covariance of INID order statistics | It is known. We can even make a more precise and slightly stronger statement.
The following demonstration rests on two ideas. The first is that for any random variable $X$, the conditional expectati | Covariance of INID order statistics
It is known. We can even make a more precise and slightly stronger statement.
The following demonstration rests on two ideas. The first is that for any random variable $X$, the conditional expectation
$$\mathbb{E}[X | X \le t]$$
is a non-decreasing function of $t$. This is geometr... | Covariance of INID order statistics
It is known. We can even make a more precise and slightly stronger statement.
The following demonstration rests on two ideas. The first is that for any random variable $X$, the conditional expectati |
40,116 | Number of trees for Random Forest optimization using recursive feature elimination | Optimizing ntree and mtry (above mtry=sqrt(#features) and ntree large enough for stabilization of OOB) is a dangerous area -- you need hard core nested cross-validation to be safe from overfitting, so you may end up doing more computations that you are trying to avoid.
I would say the better idea is not to use RFE -- w... | Number of trees for Random Forest optimization using recursive feature elimination | Optimizing ntree and mtry (above mtry=sqrt(#features) and ntree large enough for stabilization of OOB) is a dangerous area -- you need hard core nested cross-validation to be safe from overfitting, so | Number of trees for Random Forest optimization using recursive feature elimination
Optimizing ntree and mtry (above mtry=sqrt(#features) and ntree large enough for stabilization of OOB) is a dangerous area -- you need hard core nested cross-validation to be safe from overfitting, so you may end up doing more computatio... | Number of trees for Random Forest optimization using recursive feature elimination
Optimizing ntree and mtry (above mtry=sqrt(#features) and ntree large enough for stabilization of OOB) is a dangerous area -- you need hard core nested cross-validation to be safe from overfitting, so |
40,117 | Number of trees for Random Forest optimization using recursive feature elimination | I'm actually doing an experiment with this right now. I work in text classification, so my training set is typically on the order of several hundred thousand features, and I'm looking at comparing a linear SVM (optimized for the c-parameter) against the weka implementation of random forests. I'm finding that, for my da... | Number of trees for Random Forest optimization using recursive feature elimination | I'm actually doing an experiment with this right now. I work in text classification, so my training set is typically on the order of several hundred thousand features, and I'm looking at comparing a l | Number of trees for Random Forest optimization using recursive feature elimination
I'm actually doing an experiment with this right now. I work in text classification, so my training set is typically on the order of several hundred thousand features, and I'm looking at comparing a linear SVM (optimized for the c-parame... | Number of trees for Random Forest optimization using recursive feature elimination
I'm actually doing an experiment with this right now. I work in text classification, so my training set is typically on the order of several hundred thousand features, and I'm looking at comparing a l |
40,118 | What is the difference between AdaBoost.M2, AdaBoost.M1, Gentle AdaBoost, RealAdaboost and the Original Adaboost? | A Survey of several of the variants can be found in the paper,
'Survey on Boosting Algorithms for Supervised and Semi-supervised Learning,' Artur Ferreira.
Generally, the Original AdaBoost returns the binary valued class that is the ensemble sign result of several combined models.
Real AdaBoost returns a real valued pr... | What is the difference between AdaBoost.M2, AdaBoost.M1, Gentle AdaBoost, RealAdaboost and the Origi | A Survey of several of the variants can be found in the paper,
'Survey on Boosting Algorithms for Supervised and Semi-supervised Learning,' Artur Ferreira.
Generally, the Original AdaBoost returns the | What is the difference between AdaBoost.M2, AdaBoost.M1, Gentle AdaBoost, RealAdaboost and the Original Adaboost?
A Survey of several of the variants can be found in the paper,
'Survey on Boosting Algorithms for Supervised and Semi-supervised Learning,' Artur Ferreira.
Generally, the Original AdaBoost returns the binar... | What is the difference between AdaBoost.M2, AdaBoost.M1, Gentle AdaBoost, RealAdaboost and the Origi
A Survey of several of the variants can be found in the paper,
'Survey on Boosting Algorithms for Supervised and Semi-supervised Learning,' Artur Ferreira.
Generally, the Original AdaBoost returns the |
40,119 | How to calculate power (or sample size) for a multiple comparison experiment? | If you have already done the experiment then there is little point in doing any power analyses. Where the P-values are small the power for the observed effect size and variability was large enough. Where the P-values are large then the power was small for the observed effect size and variability. Power analysis is usef... | How to calculate power (or sample size) for a multiple comparison experiment? | If you have already done the experiment then there is little point in doing any power analyses. Where the P-values are small the power for the observed effect size and variability was large enough. Wh | How to calculate power (or sample size) for a multiple comparison experiment?
If you have already done the experiment then there is little point in doing any power analyses. Where the P-values are small the power for the observed effect size and variability was large enough. Where the P-values are large then the power ... | How to calculate power (or sample size) for a multiple comparison experiment?
If you have already done the experiment then there is little point in doing any power analyses. Where the P-values are small the power for the observed effect size and variability was large enough. Wh |
40,120 | How to calculate power (or sample size) for a multiple comparison experiment? | I came upon your post and I really didn't know if my answer will be of any help to you because it would actually require to reconsider the whole analysis. However: your problem appears to be purely related to data-mining since you are actually trying to uncover the number and centers of latent clusters of individuals w... | How to calculate power (or sample size) for a multiple comparison experiment? | I came upon your post and I really didn't know if my answer will be of any help to you because it would actually require to reconsider the whole analysis. However: your problem appears to be purely re | How to calculate power (or sample size) for a multiple comparison experiment?
I came upon your post and I really didn't know if my answer will be of any help to you because it would actually require to reconsider the whole analysis. However: your problem appears to be purely related to data-mining since you are actuall... | How to calculate power (or sample size) for a multiple comparison experiment?
I came upon your post and I really didn't know if my answer will be of any help to you because it would actually require to reconsider the whole analysis. However: your problem appears to be purely re |
40,121 | Understanding the output of lme | I will assume that the modeling assumptions you made are correct and you ran the program properly since your question only addresses the interpretation of the output.
In a linear model involving a single covariate, you can test for a linear association either by testing whether the slope coefficient is 0 or not or by t... | Understanding the output of lme | I will assume that the modeling assumptions you made are correct and you ran the program properly since your question only addresses the interpretation of the output.
In a linear model involving a sin | Understanding the output of lme
I will assume that the modeling assumptions you made are correct and you ran the program properly since your question only addresses the interpretation of the output.
In a linear model involving a single covariate, you can test for a linear association either by testing whether the slope... | Understanding the output of lme
I will assume that the modeling assumptions you made are correct and you ran the program properly since your question only addresses the interpretation of the output.
In a linear model involving a sin |
40,122 | Understanding the output of lme | A further clarification. Your question asks whether this test provides evidence of a linear relation. In fact, linearity is an assumption of the model - the model does not test for linearity. Therefore, it is possible that the relationship between variables is, in fact, non-linear. In this case, the model could either ... | Understanding the output of lme | A further clarification. Your question asks whether this test provides evidence of a linear relation. In fact, linearity is an assumption of the model - the model does not test for linearity. Therefor | Understanding the output of lme
A further clarification. Your question asks whether this test provides evidence of a linear relation. In fact, linearity is an assumption of the model - the model does not test for linearity. Therefore, it is possible that the relationship between variables is, in fact, non-linear. In th... | Understanding the output of lme
A further clarification. Your question asks whether this test provides evidence of a linear relation. In fact, linearity is an assumption of the model - the model does not test for linearity. Therefor |
40,123 | Identify different periods of variance in a time series | There is a lot of literature for testing the change in mean. If it is known that mean does not change, and you need to test the variance, you can convert the problem of testing for change in variance to the one of testing for change in mean with simple transformation.
Suppose your initial data is $X_i$, then define $Y... | Identify different periods of variance in a time series | There is a lot of literature for testing the change in mean. If it is known that mean does not change, and you need to test the variance, you can convert the problem of testing for change in variance | Identify different periods of variance in a time series
There is a lot of literature for testing the change in mean. If it is known that mean does not change, and you need to test the variance, you can convert the problem of testing for change in variance to the one of testing for change in mean with simple transformat... | Identify different periods of variance in a time series
There is a lot of literature for testing the change in mean. If it is known that mean does not change, and you need to test the variance, you can convert the problem of testing for change in variance |
40,124 | Identify different periods of variance in a time series | The changepoint package in R has a cpt.var function that can calculate changes in variance. There are two tests in that function one for data that can be assumed to be Normal distributed (default option dist="Normal") and one for nonparametric testing (option dist="CSS"). The CSS approach is Cumulative Sum of Squares ... | Identify different periods of variance in a time series | The changepoint package in R has a cpt.var function that can calculate changes in variance. There are two tests in that function one for data that can be assumed to be Normal distributed (default opt | Identify different periods of variance in a time series
The changepoint package in R has a cpt.var function that can calculate changes in variance. There are two tests in that function one for data that can be assumed to be Normal distributed (default option dist="Normal") and one for nonparametric testing (option dis... | Identify different periods of variance in a time series
The changepoint package in R has a cpt.var function that can calculate changes in variance. There are two tests in that function one for data that can be assumed to be Normal distributed (default opt |
40,125 | Identify different periods of variance in a time series | Variance change in a time series is discussed in http://www.unc.edu/~jbhill/tsay.pdf . This feature has been added to software available from http:..www.autobox.com. I am involved in the development/incorporation of this very important feature which has neen apparently ignored by SAS,SPSS and others. | Identify different periods of variance in a time series | Variance change in a time series is discussed in http://www.unc.edu/~jbhill/tsay.pdf . This feature has been added to software available from http:..www.autobox.com. I am involved in the development/i | Identify different periods of variance in a time series
Variance change in a time series is discussed in http://www.unc.edu/~jbhill/tsay.pdf . This feature has been added to software available from http:..www.autobox.com. I am involved in the development/incorporation of this very important feature which has neen appar... | Identify different periods of variance in a time series
Variance change in a time series is discussed in http://www.unc.edu/~jbhill/tsay.pdf . This feature has been added to software available from http:..www.autobox.com. I am involved in the development/i |
40,126 | Identify different periods of variance in a time series | You can do this using the mcp package, provided you know the number of segments in advance:
model = list(
price ~ 1 + sigma(1), # Intercept and variance
~ 0 + sigma(1), # Change in variance, but not in mean
~ 0 + sigma(1) # same
)
library(mcp)
fit = mcp(model, df, par_x = "time")
You can infer the time... | Identify different periods of variance in a time series | You can do this using the mcp package, provided you know the number of segments in advance:
model = list(
price ~ 1 + sigma(1), # Intercept and variance
~ 0 + sigma(1), # Change in variance, | Identify different periods of variance in a time series
You can do this using the mcp package, provided you know the number of segments in advance:
model = list(
price ~ 1 + sigma(1), # Intercept and variance
~ 0 + sigma(1), # Change in variance, but not in mean
~ 0 + sigma(1) # same
)
library(mcp)
fit ... | Identify different periods of variance in a time series
You can do this using the mcp package, provided you know the number of segments in advance:
model = list(
price ~ 1 + sigma(1), # Intercept and variance
~ 0 + sigma(1), # Change in variance, |
40,127 | Equivalent of Kolmogorov-Smirnov test for integer data? | The Permutation test could be applied here as well. The idea is as follows.
Let $X_1,...,X_m\sim F$ and $Y_1,...,Y_n\sim G$ be two independent samples and consider testing the hypothesis $H_0:F=G$ vs. $H_1:F\neq G$. For this purpose, label your data as follows
\begin{array}{c c}
1 & X_1\\
1 & X_2\\
\vdots & \vdots\\
1 ... | Equivalent of Kolmogorov-Smirnov test for integer data? | The Permutation test could be applied here as well. The idea is as follows.
Let $X_1,...,X_m\sim F$ and $Y_1,...,Y_n\sim G$ be two independent samples and consider testing the hypothesis $H_0:F=G$ vs. | Equivalent of Kolmogorov-Smirnov test for integer data?
The Permutation test could be applied here as well. The idea is as follows.
Let $X_1,...,X_m\sim F$ and $Y_1,...,Y_n\sim G$ be two independent samples and consider testing the hypothesis $H_0:F=G$ vs. $H_1:F\neq G$. For this purpose, label your data as follows
\be... | Equivalent of Kolmogorov-Smirnov test for integer data?
The Permutation test could be applied here as well. The idea is as follows.
Let $X_1,...,X_m\sim F$ and $Y_1,...,Y_n\sim G$ be two independent samples and consider testing the hypothesis $H_0:F=G$ vs. |
40,128 | Equivalent of Kolmogorov-Smirnov test for integer data? | I would suggest the two sample chi square test where you bin the data and compare the binned total with an "expected number" that would fall within the binbased on the pooled sample. This has a generalization to k greater than 2. I am assuming that you are not requiring another test of the emprical cdf form. I think ... | Equivalent of Kolmogorov-Smirnov test for integer data? | I would suggest the two sample chi square test where you bin the data and compare the binned total with an "expected number" that would fall within the binbased on the pooled sample. This has a gener | Equivalent of Kolmogorov-Smirnov test for integer data?
I would suggest the two sample chi square test where you bin the data and compare the binned total with an "expected number" that would fall within the binbased on the pooled sample. This has a generalization to k greater than 2. I am assuming that you are not re... | Equivalent of Kolmogorov-Smirnov test for integer data?
I would suggest the two sample chi square test where you bin the data and compare the binned total with an "expected number" that would fall within the binbased on the pooled sample. This has a gener |
40,129 | A problem in probability theory | You will need the following simple result.
Lemma. If $\mathrm{Var}[Z]=0$, then $Z=\mathrm{E}[Z]$, almost surely.
Proof (check cardinal's comment for a contrapositive argument). It is easy to check that
$$
\left\{ Z = \mathrm{E}[Z] \right\} = \bigcap_{n\geq 1} \left\{ |Z - \mathrm{E}[Z]| < \frac{1}{n} \right\} \, .
$$... | A problem in probability theory | You will need the following simple result.
Lemma. If $\mathrm{Var}[Z]=0$, then $Z=\mathrm{E}[Z]$, almost surely.
Proof (check cardinal's comment for a contrapositive argument). It is easy to check tha | A problem in probability theory
You will need the following simple result.
Lemma. If $\mathrm{Var}[Z]=0$, then $Z=\mathrm{E}[Z]$, almost surely.
Proof (check cardinal's comment for a contrapositive argument). It is easy to check that
$$
\left\{ Z = \mathrm{E}[Z] \right\} = \bigcap_{n\geq 1} \left\{ |Z - \mathrm{E}[Z]... | A problem in probability theory
You will need the following simple result.
Lemma. If $\mathrm{Var}[Z]=0$, then $Z=\mathrm{E}[Z]$, almost surely.
Proof (check cardinal's comment for a contrapositive argument). It is easy to check tha |
40,130 | Confusion regarding correlation and mse | I suggest we look at the definitions.
$$ MSE = \frac{1}{n-2}\sum_{i=1}^n (Y_i -\hat{Y}_i)^2. $$
Again, by definition
$$ \hat{Y}_i = r\frac{S_y}{S_x}(X_i - \bar{X}) + \bar{Y}, $$
so
$$SSE = \sum_{i=1}^n \left( (Y_i - \bar{Y}) - r\frac{S_y}{S_x}(X_i - \bar{X}) \right) ^2 = (n-1)S_y^2 + (n-1)r^2S_y^2 - 2(n-1)r^2S_y^2,$$
... | Confusion regarding correlation and mse | I suggest we look at the definitions.
$$ MSE = \frac{1}{n-2}\sum_{i=1}^n (Y_i -\hat{Y}_i)^2. $$
Again, by definition
$$ \hat{Y}_i = r\frac{S_y}{S_x}(X_i - \bar{X}) + \bar{Y}, $$
so
$$SSE = \sum_{i=1}^ | Confusion regarding correlation and mse
I suggest we look at the definitions.
$$ MSE = \frac{1}{n-2}\sum_{i=1}^n (Y_i -\hat{Y}_i)^2. $$
Again, by definition
$$ \hat{Y}_i = r\frac{S_y}{S_x}(X_i - \bar{X}) + \bar{Y}, $$
so
$$SSE = \sum_{i=1}^n \left( (Y_i - \bar{Y}) - r\frac{S_y}{S_x}(X_i - \bar{X}) \right) ^2 = (n-1)S_... | Confusion regarding correlation and mse
I suggest we look at the definitions.
$$ MSE = \frac{1}{n-2}\sum_{i=1}^n (Y_i -\hat{Y}_i)^2. $$
Again, by definition
$$ \hat{Y}_i = r\frac{S_y}{S_x}(X_i - \bar{X}) + \bar{Y}, $$
so
$$SSE = \sum_{i=1}^ |
40,131 | Confusion regarding correlation and mse | Correlation: The degree or extent to which variables are linearly related is called the correlation among variables.
MSE: If $T$ be an estimator of $\theta$. $MSE(T)$ is a measure of the spread of the values of $T$ around $\theta$
These two are different so don't be confused | Confusion regarding correlation and mse | Correlation: The degree or extent to which variables are linearly related is called the correlation among variables.
MSE: If $T$ be an estimator of $\theta$. $MSE(T)$ is a measure of the spread of the | Confusion regarding correlation and mse
Correlation: The degree or extent to which variables are linearly related is called the correlation among variables.
MSE: If $T$ be an estimator of $\theta$. $MSE(T)$ is a measure of the spread of the values of $T$ around $\theta$
These two are different so don't be confused | Confusion regarding correlation and mse
Correlation: The degree or extent to which variables are linearly related is called the correlation among variables.
MSE: If $T$ be an estimator of $\theta$. $MSE(T)$ is a measure of the spread of the |
40,132 | What is the meaning of orthogonal in validation testing? | Basically, it would seem that people use orthogonal as a synonym for independent. So, for orthogonal validation read independent validation The validity of equating orthogonality with independence is discussed here such that "if X and Y are independent then they are Orthogonal" but "the converse is not true". | What is the meaning of orthogonal in validation testing? | Basically, it would seem that people use orthogonal as a synonym for independent. So, for orthogonal validation read independent validation The validity of equating orthogonality with independence is | What is the meaning of orthogonal in validation testing?
Basically, it would seem that people use orthogonal as a synonym for independent. So, for orthogonal validation read independent validation The validity of equating orthogonality with independence is discussed here such that "if X and Y are independent then they ... | What is the meaning of orthogonal in validation testing?
Basically, it would seem that people use orthogonal as a synonym for independent. So, for orthogonal validation read independent validation The validity of equating orthogonality with independence is |
40,133 | What is the meaning of orthogonal in validation testing? | An orthogonal method is an additional method that provides very different selectivity to the primary method. The orthogonal method can be used to evaluate the primary method. For example, two methods can be used to investigate protein aggregation 1) size-exclusion chromatograph or an orthogonal method such as 2) analy... | What is the meaning of orthogonal in validation testing? | An orthogonal method is an additional method that provides very different selectivity to the primary method. The orthogonal method can be used to evaluate the primary method. For example, two methods | What is the meaning of orthogonal in validation testing?
An orthogonal method is an additional method that provides very different selectivity to the primary method. The orthogonal method can be used to evaluate the primary method. For example, two methods can be used to investigate protein aggregation 1) size-exclusi... | What is the meaning of orthogonal in validation testing?
An orthogonal method is an additional method that provides very different selectivity to the primary method. The orthogonal method can be used to evaluate the primary method. For example, two methods |
40,134 | What is the meaning of orthogonal in validation testing? | This "orthogonal" word is rather fashionable part of slang in recent EMA/FDA guidelines. Literary means that something crossed at right angle, so more intuitively understood substitute is "cross-over methodology" i.e. two essentially different methods used to measure the same value ("crossing-point"), so the measuremen... | What is the meaning of orthogonal in validation testing? | This "orthogonal" word is rather fashionable part of slang in recent EMA/FDA guidelines. Literary means that something crossed at right angle, so more intuitively understood substitute is "cross-over | What is the meaning of orthogonal in validation testing?
This "orthogonal" word is rather fashionable part of slang in recent EMA/FDA guidelines. Literary means that something crossed at right angle, so more intuitively understood substitute is "cross-over methodology" i.e. two essentially different methods used to mea... | What is the meaning of orthogonal in validation testing?
This "orthogonal" word is rather fashionable part of slang in recent EMA/FDA guidelines. Literary means that something crossed at right angle, so more intuitively understood substitute is "cross-over |
40,135 | What is the meaning of orthogonal in validation testing? | Orthogonal means independent in loose sense. It sounds different in different contexts and technical fields. like in signal processing orthogonal signals means the signals with zero correlation.
But in the contest of testing (validation and verification of embedded software or hardware), preparing and testing more tha... | What is the meaning of orthogonal in validation testing? | Orthogonal means independent in loose sense. It sounds different in different contexts and technical fields. like in signal processing orthogonal signals means the signals with zero correlation.
But | What is the meaning of orthogonal in validation testing?
Orthogonal means independent in loose sense. It sounds different in different contexts and technical fields. like in signal processing orthogonal signals means the signals with zero correlation.
But in the contest of testing (validation and verification of embed... | What is the meaning of orthogonal in validation testing?
Orthogonal means independent in loose sense. It sounds different in different contexts and technical fields. like in signal processing orthogonal signals means the signals with zero correlation.
But |
40,136 | Confidence interval for the height of a histogram bar | Let there be $i \in 1, \dots, I$ histogram bins. The probability of falling into a particular bin is $p_i$. This is just a binomial trial (i.e., you are either in the bin or not, each with a given probability).
If you are calculating the frequency of being in the bin (i.e., histograms with bars giving the set of $p_i$... | Confidence interval for the height of a histogram bar | Let there be $i \in 1, \dots, I$ histogram bins. The probability of falling into a particular bin is $p_i$. This is just a binomial trial (i.e., you are either in the bin or not, each with a given pro | Confidence interval for the height of a histogram bar
Let there be $i \in 1, \dots, I$ histogram bins. The probability of falling into a particular bin is $p_i$. This is just a binomial trial (i.e., you are either in the bin or not, each with a given probability).
If you are calculating the frequency of being in the b... | Confidence interval for the height of a histogram bar
Let there be $i \in 1, \dots, I$ histogram bins. The probability of falling into a particular bin is $p_i$. This is just a binomial trial (i.e., you are either in the bin or not, each with a given pro |
40,137 | Confidence interval for the height of a histogram bar | Using the binomial variance $p (1-p) k$, as proposed in another answer, is only a good idea when the proportion is not near 0 or 1.
For better-behaved confidence intervals, there is extensive statistical literature e.g. Agresti & Coull (1998). Some of the proposed formulae are implemented in the R library PropCIs. Her... | Confidence interval for the height of a histogram bar | Using the binomial variance $p (1-p) k$, as proposed in another answer, is only a good idea when the proportion is not near 0 or 1.
For better-behaved confidence intervals, there is extensive statist | Confidence interval for the height of a histogram bar
Using the binomial variance $p (1-p) k$, as proposed in another answer, is only a good idea when the proportion is not near 0 or 1.
For better-behaved confidence intervals, there is extensive statistical literature e.g. Agresti & Coull (1998). Some of the proposed ... | Confidence interval for the height of a histogram bar
Using the binomial variance $p (1-p) k$, as proposed in another answer, is only a good idea when the proportion is not near 0 or 1.
For better-behaved confidence intervals, there is extensive statist |
40,138 | Calculate number of needed simulations | I try to summarize all answers by you in order to have a single place for everything important.
Steps to calculate the needed number of simulations:
Run the simulation with a default number of runs $R_0$. I've seen $R_0 = 1000$ most of the times. Now you should have a vector with the results $x_0$ where $length(x_0) =... | Calculate number of needed simulations | I try to summarize all answers by you in order to have a single place for everything important.
Steps to calculate the needed number of simulations:
Run the simulation with a default number of runs $ | Calculate number of needed simulations
I try to summarize all answers by you in order to have a single place for everything important.
Steps to calculate the needed number of simulations:
Run the simulation with a default number of runs $R_0$. I've seen $R_0 = 1000$ most of the times. Now you should have a vector with... | Calculate number of needed simulations
I try to summarize all answers by you in order to have a single place for everything important.
Steps to calculate the needed number of simulations:
Run the simulation with a default number of runs $ |
40,139 | Calculate number of needed simulations | This question is more difficult to answer than you imagine. It depends on the input, the output and degree of precision required on the output. One thing to do is add say $100$ simulations to the current number and if the results seem not to have changed much there could be sufficient convergence. If not keep going ... | Calculate number of needed simulations | This question is more difficult to answer than you imagine. It depends on the input, the output and degree of precision required on the output. One thing to do is add say $100$ simulations to the cu | Calculate number of needed simulations
This question is more difficult to answer than you imagine. It depends on the input, the output and degree of precision required on the output. One thing to do is add say $100$ simulations to the current number and if the results seem not to have changed much there could be suff... | Calculate number of needed simulations
This question is more difficult to answer than you imagine. It depends on the input, the output and degree of precision required on the output. One thing to do is add say $100$ simulations to the cu |
40,140 | Calculate number of needed simulations | Powerbar, this answer is to address your last comment. Perhaps you can think of your simulation as representing a model for fuel consumption as a function of % minivans on the road plus a random noise component. So every time you run the simulation you get a slightly distorted picture of the curve because of the addi... | Calculate number of needed simulations | Powerbar, this answer is to address your last comment. Perhaps you can think of your simulation as representing a model for fuel consumption as a function of % minivans on the road plus a random nois | Calculate number of needed simulations
Powerbar, this answer is to address your last comment. Perhaps you can think of your simulation as representing a model for fuel consumption as a function of % minivans on the road plus a random noise component. So every time you run the simulation you get a slightly distorted p... | Calculate number of needed simulations
Powerbar, this answer is to address your last comment. Perhaps you can think of your simulation as representing a model for fuel consumption as a function of % minivans on the road plus a random nois |
40,141 | Differencing an i.i.d. time series | If I understand your comments correctly, you've overdifferenced, which is talked about in various guides.
EDIT:
Your original series of numbers (rnorm(5000, 0, 40)) has, by definition and design, no relationship between adjacent numbers or every 2nd number or every 3rd number. It's "random" (pseudo-random, but not dis... | Differencing an i.i.d. time series | If I understand your comments correctly, you've overdifferenced, which is talked about in various guides.
EDIT:
Your original series of numbers (rnorm(5000, 0, 40)) has, by definition and design, no | Differencing an i.i.d. time series
If I understand your comments correctly, you've overdifferenced, which is talked about in various guides.
EDIT:
Your original series of numbers (rnorm(5000, 0, 40)) has, by definition and design, no relationship between adjacent numbers or every 2nd number or every 3rd number. It's "... | Differencing an i.i.d. time series
If I understand your comments correctly, you've overdifferenced, which is talked about in various guides.
EDIT:
Your original series of numbers (rnorm(5000, 0, 40)) has, by definition and design, no |
40,142 | Differencing an i.i.d. time series | It is a little late ..... but , review the Slutsky Effect where a linear (weighted ) combinations of i.i.d. values leads to a series with auto-correlative structure. This is why assuming any filter picket out of the blue can be dangerous. X11-ARIMA assumes a 16 period equally weighted average ( you can change 16 to an... | Differencing an i.i.d. time series | It is a little late ..... but , review the Slutsky Effect where a linear (weighted ) combinations of i.i.d. values leads to a series with auto-correlative structure. This is why assuming any filter pi | Differencing an i.i.d. time series
It is a little late ..... but , review the Slutsky Effect where a linear (weighted ) combinations of i.i.d. values leads to a series with auto-correlative structure. This is why assuming any filter picket out of the blue can be dangerous. X11-ARIMA assumes a 16 period equally weighte... | Differencing an i.i.d. time series
It is a little late ..... but , review the Slutsky Effect where a linear (weighted ) combinations of i.i.d. values leads to a series with auto-correlative structure. This is why assuming any filter pi |
40,143 | Expectation of an estimator? | An estimator is not a parameter, but a random variable. Basically, your estimate depends on the sample which is random, and this makes your estimate a realisation of a random variable called estimator. | Expectation of an estimator? | An estimator is not a parameter, but a random variable. Basically, your estimate depends on the sample which is random, and this makes your estimate a realisation of a random variable called estimator | Expectation of an estimator?
An estimator is not a parameter, but a random variable. Basically, your estimate depends on the sample which is random, and this makes your estimate a realisation of a random variable called estimator. | Expectation of an estimator?
An estimator is not a parameter, but a random variable. Basically, your estimate depends on the sample which is random, and this makes your estimate a realisation of a random variable called estimator |
40,144 | Understanding Odds Ratios in Logistic Regression | If you're using the equation you list below your code, I think you're OK. It's true that the numbers inside that equation are log odds, but once you've solved for $\text{Pr}(Y=1)$, you do have a probability. As far as I can tell, you are not misinterpreting your results. | Understanding Odds Ratios in Logistic Regression | If you're using the equation you list below your code, I think you're OK. It's true that the numbers inside that equation are log odds, but once you've solved for $\text{Pr}(Y=1)$, you do have a prob | Understanding Odds Ratios in Logistic Regression
If you're using the equation you list below your code, I think you're OK. It's true that the numbers inside that equation are log odds, but once you've solved for $\text{Pr}(Y=1)$, you do have a probability. As far as I can tell, you are not misinterpreting your result... | Understanding Odds Ratios in Logistic Regression
If you're using the equation you list below your code, I think you're OK. It's true that the numbers inside that equation are log odds, but once you've solved for $\text{Pr}(Y=1)$, you do have a prob |
40,145 | Understanding Odds Ratios in Logistic Regression | I think it depends on what you mean with interpreting. If you are trying to understand whether the equation for $Pr(Y=1)$ is correct, then I would say yes.
If you want to find an interpretation of the equation itself, then you should consider odds ratios; a unital increase of bid (here you have no problem in fixing all... | Understanding Odds Ratios in Logistic Regression | I think it depends on what you mean with interpreting. If you are trying to understand whether the equation for $Pr(Y=1)$ is correct, then I would say yes.
If you want to find an interpretation of the | Understanding Odds Ratios in Logistic Regression
I think it depends on what you mean with interpreting. If you are trying to understand whether the equation for $Pr(Y=1)$ is correct, then I would say yes.
If you want to find an interpretation of the equation itself, then you should consider odds ratios; a unital increa... | Understanding Odds Ratios in Logistic Regression
I think it depends on what you mean with interpreting. If you are trying to understand whether the equation for $Pr(Y=1)$ is correct, then I would say yes.
If you want to find an interpretation of the |
40,146 | What is the probability of rolling all faces of a die after n number of rolls | So you want to know the probability of getting all the faces at least once after rolling the die $n$ times. It is convenient to introduce the number $N_k$ of faces that have been seen after $k$ steps. Obviously, we have $N_1=1$. Also, $N_{k+1}=N_k$ with probability $\frac{N_k}{6}$ and $N_{k+1}=N_k+1$ otherwise -- in ot... | What is the probability of rolling all faces of a die after n number of rolls | So you want to know the probability of getting all the faces at least once after rolling the die $n$ times. It is convenient to introduce the number $N_k$ of faces that have been seen after $k$ steps. | What is the probability of rolling all faces of a die after n number of rolls
So you want to know the probability of getting all the faces at least once after rolling the die $n$ times. It is convenient to introduce the number $N_k$ of faces that have been seen after $k$ steps. Obviously, we have $N_1=1$. Also, $N_{k+1... | What is the probability of rolling all faces of a die after n number of rolls
So you want to know the probability of getting all the faces at least once after rolling the die $n$ times. It is convenient to introduce the number $N_k$ of faces that have been seen after $k$ steps. |
40,147 | Algorithms for clustering documents by similar words and phrases | Right off the bat, you may want to look at various string distances. The only one I'm familiar with is the Levenshtein distance, which is pretty rudimentary. You could apply this on sentences or phrases.
You may want to take a look at some natural language processing techniques, too, such as stemming and tokenizing you... | Algorithms for clustering documents by similar words and phrases | Right off the bat, you may want to look at various string distances. The only one I'm familiar with is the Levenshtein distance, which is pretty rudimentary. You could apply this on sentences or phras | Algorithms for clustering documents by similar words and phrases
Right off the bat, you may want to look at various string distances. The only one I'm familiar with is the Levenshtein distance, which is pretty rudimentary. You could apply this on sentences or phrases.
You may want to take a look at some natural languag... | Algorithms for clustering documents by similar words and phrases
Right off the bat, you may want to look at various string distances. The only one I'm familiar with is the Levenshtein distance, which is pretty rudimentary. You could apply this on sentences or phras |
40,148 | Algorithms for clustering documents by similar words and phrases | For pretty much any clustering algorithm that can work with such data, you will need to define a distance or similarity function first. So you might want to browse through the literature on appropriate distance functions for your task.
E.g. cosine distance on a TF-IDF normalized vector representation. | Algorithms for clustering documents by similar words and phrases | For pretty much any clustering algorithm that can work with such data, you will need to define a distance or similarity function first. So you might want to browse through the literature on appropriat | Algorithms for clustering documents by similar words and phrases
For pretty much any clustering algorithm that can work with such data, you will need to define a distance or similarity function first. So you might want to browse through the literature on appropriate distance functions for your task.
E.g. cosine distanc... | Algorithms for clustering documents by similar words and phrases
For pretty much any clustering algorithm that can work with such data, you will need to define a distance or similarity function first. So you might want to browse through the literature on appropriat |
40,149 | Mining search logs to improve autocomplete suggestions? | it is a matter of ordering correlations based on statistical significance, and generating enough data over time to define that significance. the noise (random correlations) will be filtered out as more people search for, and correlate, terms and characters.
auto-complete should return the top n results as a user is ent... | Mining search logs to improve autocomplete suggestions? | it is a matter of ordering correlations based on statistical significance, and generating enough data over time to define that significance. the noise (random correlations) will be filtered out as mor | Mining search logs to improve autocomplete suggestions?
it is a matter of ordering correlations based on statistical significance, and generating enough data over time to define that significance. the noise (random correlations) will be filtered out as more people search for, and correlate, terms and characters.
auto-c... | Mining search logs to improve autocomplete suggestions?
it is a matter of ordering correlations based on statistical significance, and generating enough data over time to define that significance. the noise (random correlations) will be filtered out as mor |
40,150 | Mining search logs to improve autocomplete suggestions? | Interesting project. The technique that comes to mind my mind is association mining.
This technique can automatically discover many many patterns in data of this kind. It is often used in retail market research, where the question is "If a shopper bought 10 products, which of them were purchased 'together' and which j... | Mining search logs to improve autocomplete suggestions? | Interesting project. The technique that comes to mind my mind is association mining.
This technique can automatically discover many many patterns in data of this kind. It is often used in retail mark | Mining search logs to improve autocomplete suggestions?
Interesting project. The technique that comes to mind my mind is association mining.
This technique can automatically discover many many patterns in data of this kind. It is often used in retail market research, where the question is "If a shopper bought 10 produ... | Mining search logs to improve autocomplete suggestions?
Interesting project. The technique that comes to mind my mind is association mining.
This technique can automatically discover many many patterns in data of this kind. It is often used in retail mark |
40,151 | Mining search logs to improve autocomplete suggestions? | Is there a similarity measure that you can use for character names? Besides that, I feel that you will need some kind of feedback here: Basically, you need to prove or disprove every correlation (here: equivalence) that you are assuming from the data.
Imagine that a user enters A' to find character A, and then B' to fi... | Mining search logs to improve autocomplete suggestions? | Is there a similarity measure that you can use for character names? Besides that, I feel that you will need some kind of feedback here: Basically, you need to prove or disprove every correlation (here | Mining search logs to improve autocomplete suggestions?
Is there a similarity measure that you can use for character names? Besides that, I feel that you will need some kind of feedback here: Basically, you need to prove or disprove every correlation (here: equivalence) that you are assuming from the data.
Imagine that... | Mining search logs to improve autocomplete suggestions?
Is there a similarity measure that you can use for character names? Besides that, I feel that you will need some kind of feedback here: Basically, you need to prove or disprove every correlation (here |
40,152 | Simulating responses to a test for item-response theory | I've included a simdata() function in the mirt package in R for calculating simulated IRT data given a variety of know conditions for several different classes of uni- and multidimensional IRT models. So if you need something a little more flexible that may be a good place to look, and should save you from having to re... | Simulating responses to a test for item-response theory | I've included a simdata() function in the mirt package in R for calculating simulated IRT data given a variety of know conditions for several different classes of uni- and multidimensional IRT models. | Simulating responses to a test for item-response theory
I've included a simdata() function in the mirt package in R for calculating simulated IRT data given a variety of know conditions for several different classes of uni- and multidimensional IRT models. So if you need something a little more flexible that may be a g... | Simulating responses to a test for item-response theory
I've included a simdata() function in the mirt package in R for calculating simulated IRT data given a variety of know conditions for several different classes of uni- and multidimensional IRT models. |
40,153 | Simulating responses to a test for item-response theory | Here's a first version of this simulation in R, as an example of what NOT to do. Code untested.
# we're making a table of three columns: person, question, and correct or not
resps <- data.frame(person=integer(), question=integer(), correct=integer())
# do the simulation
for (qu in 1:nQ) { # loop over questions, nQ is ... | Simulating responses to a test for item-response theory | Here's a first version of this simulation in R, as an example of what NOT to do. Code untested.
# we're making a table of three columns: person, question, and correct or not
resps <- data.frame(person | Simulating responses to a test for item-response theory
Here's a first version of this simulation in R, as an example of what NOT to do. Code untested.
# we're making a table of three columns: person, question, and correct or not
resps <- data.frame(person=integer(), question=integer(), correct=integer())
# do the sim... | Simulating responses to a test for item-response theory
Here's a first version of this simulation in R, as an example of what NOT to do. Code untested.
# we're making a table of three columns: person, question, and correct or not
resps <- data.frame(person |
40,154 | Simulating responses to a test for item-response theory | The easiest way to do what you want is probably to use the psych package for R. This includes functions such as sim.rasch and sim.irt which will simulate appropriate data of whatever size for you. | Simulating responses to a test for item-response theory | The easiest way to do what you want is probably to use the psych package for R. This includes functions such as sim.rasch and sim.irt which will simulate appropriate data of whatever size for you. | Simulating responses to a test for item-response theory
The easiest way to do what you want is probably to use the psych package for R. This includes functions such as sim.rasch and sim.irt which will simulate appropriate data of whatever size for you. | Simulating responses to a test for item-response theory
The easiest way to do what you want is probably to use the psych package for R. This includes functions such as sim.rasch and sim.irt which will simulate appropriate data of whatever size for you. |
40,155 | Simulating responses to a test for item-response theory | I know it is a bit too late, but for historical reasons I'd like to answer this question.
Simulating a CAT using an R package
Nowadays, the package catr is able to simulate CATs, which, it seems, is exactly what you want to do. It has a lot of options, like number of starting itens, selection method for the next item, ... | Simulating responses to a test for item-response theory | I know it is a bit too late, but for historical reasons I'd like to answer this question.
Simulating a CAT using an R package
Nowadays, the package catr is able to simulate CATs, which, it seems, is e | Simulating responses to a test for item-response theory
I know it is a bit too late, but for historical reasons I'd like to answer this question.
Simulating a CAT using an R package
Nowadays, the package catr is able to simulate CATs, which, it seems, is exactly what you want to do. It has a lot of options, like number... | Simulating responses to a test for item-response theory
I know it is a bit too late, but for historical reasons I'd like to answer this question.
Simulating a CAT using an R package
Nowadays, the package catr is able to simulate CATs, which, it seems, is e |
40,156 | Convergence of a genetic algorithm | A simple and common test is to measure improvements in the objective functions: if you no longer improve (by a certain amount) over a set number of iterations, you may as well stop. Other optimisation algorithms use this approach too. | Convergence of a genetic algorithm | A simple and common test is to measure improvements in the objective functions: if you no longer improve (by a certain amount) over a set number of iterations, you may as well stop. Other optimisation | Convergence of a genetic algorithm
A simple and common test is to measure improvements in the objective functions: if you no longer improve (by a certain amount) over a set number of iterations, you may as well stop. Other optimisation algorithms use this approach too. | Convergence of a genetic algorithm
A simple and common test is to measure improvements in the objective functions: if you no longer improve (by a certain amount) over a set number of iterations, you may as well stop. Other optimisation |
40,157 | Convergence of a genetic algorithm | If you don't have any clue on the fitness landscape, i.e. existence of local optima, plateaus, valleys etc, it is hard to understand whether a GA (or other evolutionary algorithms, EAs) have found the global optima. You can use a multi-populations approach, e.g. an island-based GA, and then, with a specific migration s... | Convergence of a genetic algorithm | If you don't have any clue on the fitness landscape, i.e. existence of local optima, plateaus, valleys etc, it is hard to understand whether a GA (or other evolutionary algorithms, EAs) have found the | Convergence of a genetic algorithm
If you don't have any clue on the fitness landscape, i.e. existence of local optima, plateaus, valleys etc, it is hard to understand whether a GA (or other evolutionary algorithms, EAs) have found the global optima. You can use a multi-populations approach, e.g. an island-based GA, an... | Convergence of a genetic algorithm
If you don't have any clue on the fitness landscape, i.e. existence of local optima, plateaus, valleys etc, it is hard to understand whether a GA (or other evolutionary algorithms, EAs) have found the |
40,158 | Convergence of a genetic algorithm | The inherent stochasticity of genetic algorithms is what makes them such a powerful tool, however, this property also makes it difficult to know when a global minimum has been found. For example you could sit on a generation at a local minima for a long time before a lucky mutation kicks you out of it and on to a bette... | Convergence of a genetic algorithm | The inherent stochasticity of genetic algorithms is what makes them such a powerful tool, however, this property also makes it difficult to know when a global minimum has been found. For example you c | Convergence of a genetic algorithm
The inherent stochasticity of genetic algorithms is what makes them such a powerful tool, however, this property also makes it difficult to know when a global minimum has been found. For example you could sit on a generation at a local minima for a long time before a lucky mutation ki... | Convergence of a genetic algorithm
The inherent stochasticity of genetic algorithms is what makes them such a powerful tool, however, this property also makes it difficult to know when a global minimum has been found. For example you c |
40,159 | Convergence of a genetic algorithm | Theoretically (and possibly ironically), it is impossible to determine whether your GA's final solution is either a local optimum, the global optimum or anything else in the case of you don't know the number of optima and where they occur.
But you can reduce all possible outcomes, that is, after performing a GA searc... | Convergence of a genetic algorithm | Theoretically (and possibly ironically), it is impossible to determine whether your GA's final solution is either a local optimum, the global optimum or anything else in the case of you don't know the | Convergence of a genetic algorithm
Theoretically (and possibly ironically), it is impossible to determine whether your GA's final solution is either a local optimum, the global optimum or anything else in the case of you don't know the number of optima and where they occur.
But you can reduce all possible outcomes, t... | Convergence of a genetic algorithm
Theoretically (and possibly ironically), it is impossible to determine whether your GA's final solution is either a local optimum, the global optimum or anything else in the case of you don't know the |
40,160 | GLM on unbalanced design | Regression models allow you to borrow information explicitly across groups defined by your predictors. Having balanced design only means that all such groups have effects estimated with equal precision (under regression assumptions: correct mean model, homoskedasticity). This is rarely, if ever, necessary for justifyin... | GLM on unbalanced design | Regression models allow you to borrow information explicitly across groups defined by your predictors. Having balanced design only means that all such groups have effects estimated with equal precisio | GLM on unbalanced design
Regression models allow you to borrow information explicitly across groups defined by your predictors. Having balanced design only means that all such groups have effects estimated with equal precision (under regression assumptions: correct mean model, homoskedasticity). This is rarely, if ever... | GLM on unbalanced design
Regression models allow you to borrow information explicitly across groups defined by your predictors. Having balanced design only means that all such groups have effects estimated with equal precisio |
40,161 | GLM on unbalanced design | The normal GLM methods work just fine on unbalanced designs (provided you aren't using expressions that are simplified for the balanced case --- decent software will work right). Unbalanced designs just have less power than balanced designs, but 250 vs 200 isn't going to have that much of an effect. | GLM on unbalanced design | The normal GLM methods work just fine on unbalanced designs (provided you aren't using expressions that are simplified for the balanced case --- decent software will work right). Unbalanced designs ju | GLM on unbalanced design
The normal GLM methods work just fine on unbalanced designs (provided you aren't using expressions that are simplified for the balanced case --- decent software will work right). Unbalanced designs just have less power than balanced designs, but 250 vs 200 isn't going to have that much of an ef... | GLM on unbalanced design
The normal GLM methods work just fine on unbalanced designs (provided you aren't using expressions that are simplified for the balanced case --- decent software will work right). Unbalanced designs ju |
40,162 | Are real and imaginary components of frequency element of fft correlated? | The ultimate line in the question,
...the default assumption ... that the variables analyzed have a multivariate Gaussian distribution
gives us the information needed to interpret and answer it. To avoid abstractions that might obscure the simplicity of the situation, let us consider a specific example of a time ser... | Are real and imaginary components of frequency element of fft correlated? | The ultimate line in the question,
...the default assumption ... that the variables analyzed have a multivariate Gaussian distribution
gives us the information needed to interpret and answer it. To | Are real and imaginary components of frequency element of fft correlated?
The ultimate line in the question,
...the default assumption ... that the variables analyzed have a multivariate Gaussian distribution
gives us the information needed to interpret and answer it. To avoid abstractions that might obscure the sim... | Are real and imaginary components of frequency element of fft correlated?
The ultimate line in the question,
...the default assumption ... that the variables analyzed have a multivariate Gaussian distribution
gives us the information needed to interpret and answer it. To |
40,163 | Can I pass an "at" parameter for the x-axis locations of bars, to an R barplot? | barplot() is just a wrapper for rect(), so you could add the bars yourself. This could be a start:
x <- sort(sample(1:100, 10, replace=FALSE)) # x-coordinates
y <- log(x) # y-coordinates
yD <- c(0, 2*diff(y)) # twice the change between steps
barW <- 1 ... | Can I pass an "at" parameter for the x-axis locations of bars, to an R barplot? | barplot() is just a wrapper for rect(), so you could add the bars yourself. This could be a start:
x <- sort(sample(1:100, 10, replace=FALSE)) # x-coordinates
y <- log(x) | Can I pass an "at" parameter for the x-axis locations of bars, to an R barplot?
barplot() is just a wrapper for rect(), so you could add the bars yourself. This could be a start:
x <- sort(sample(1:100, 10, replace=FALSE)) # x-coordinates
y <- log(x) # y-coordinates
yD <- c(0, 2*... | Can I pass an "at" parameter for the x-axis locations of bars, to an R barplot?
barplot() is just a wrapper for rect(), so you could add the bars yourself. This could be a start:
x <- sort(sample(1:100, 10, replace=FALSE)) # x-coordinates
y <- log(x) |
40,164 | libsvm training very slow on 100K rows, suggestions? | I've seen liblinear runtimes very sensitive to tol; try tol=.1,
and if possible linear not rbf. How many classes do you have ?
How much memory do you have ? Monitor real / virtual with "top" or the like.
Stochastic gradient descent,
SGDClassifier
in scikits.learn is fast.
For example, on Mnist handwritten digit data, 1... | libsvm training very slow on 100K rows, suggestions? | I've seen liblinear runtimes very sensitive to tol; try tol=.1,
and if possible linear not rbf. How many classes do you have ?
How much memory do you have ? Monitor real / virtual with "top" or the li | libsvm training very slow on 100K rows, suggestions?
I've seen liblinear runtimes very sensitive to tol; try tol=.1,
and if possible linear not rbf. How many classes do you have ?
How much memory do you have ? Monitor real / virtual with "top" or the like.
Stochastic gradient descent,
SGDClassifier
in scikits.learn is ... | libsvm training very slow on 100K rows, suggestions?
I've seen liblinear runtimes very sensitive to tol; try tol=.1,
and if possible linear not rbf. How many classes do you have ?
How much memory do you have ? Monitor real / virtual with "top" or the li |
40,165 | When is the maximum value of chi square achieved for a non-symmetric table? | Assume we have an $I \times J$ table of relative frequencies $f_{ij} \; (1 \leq i \leq I, 1 \leq j \leq J)$, where (without loss of generality) $I < J$:
$
\begin{array}{ccccc|l}
f_{11} & \ldots & f_{1j} & \ldots & f_{1J} & f_{1.} \\
\vdots & \ddots & \vdots & \ddots & \vdots & \vdots \\
f_{i1} & \ldots & f_{ij} & \... | When is the maximum value of chi square achieved for a non-symmetric table? | Assume we have an $I \times J$ table of relative frequencies $f_{ij} \; (1 \leq i \leq I, 1 \leq j \leq J)$, where (without loss of generality) $I < J$:
$
\begin{array}{ccccc|l}
f_{11} & \ldots & f_ | When is the maximum value of chi square achieved for a non-symmetric table?
Assume we have an $I \times J$ table of relative frequencies $f_{ij} \; (1 \leq i \leq I, 1 \leq j \leq J)$, where (without loss of generality) $I < J$:
$
\begin{array}{ccccc|l}
f_{11} & \ldots & f_{1j} & \ldots & f_{1J} & f_{1.} \\
\vdots &... | When is the maximum value of chi square achieved for a non-symmetric table?
Assume we have an $I \times J$ table of relative frequencies $f_{ij} \; (1 \leq i \leq I, 1 \leq j \leq J)$, where (without loss of generality) $I < J$:
$
\begin{array}{ccccc|l}
f_{11} & \ldots & f_ |
40,166 | Difference between temporal trends | Let's start with some considerations:
One usually begins with simple reasonable models, as suggested by theory and restricted by data limitations, and moves to more complex models only if the simpler ones are inadequate. This is how statistical analysis operationalizes the scientific call for parsimony.
Fitting a tre... | Difference between temporal trends | Let's start with some considerations:
One usually begins with simple reasonable models, as suggested by theory and restricted by data limitations, and moves to more complex models only if the simpler | Difference between temporal trends
Let's start with some considerations:
One usually begins with simple reasonable models, as suggested by theory and restricted by data limitations, and moves to more complex models only if the simpler ones are inadequate. This is how statistical analysis operationalizes the scientifi... | Difference between temporal trends
Let's start with some considerations:
One usually begins with simple reasonable models, as suggested by theory and restricted by data limitations, and moves to more complex models only if the simpler |
40,167 | Difference between temporal trends | Well John you might be at risk using a single trend model. If your data has a level shift you may be falsely concluding about a persistent change over time. If your data is more representative of y(t)=y(t-1) + constant your trend model doesn't apply. I could go on for days about how trend models of the form y(t)=a+b*t ... | Difference between temporal trends | Well John you might be at risk using a single trend model. If your data has a level shift you may be falsely concluding about a persistent change over time. If your data is more representative of y(t) | Difference between temporal trends
Well John you might be at risk using a single trend model. If your data has a level shift you may be falsely concluding about a persistent change over time. If your data is more representative of y(t)=y(t-1) + constant your trend model doesn't apply. I could go on for days about how t... | Difference between temporal trends
Well John you might be at risk using a single trend model. If your data has a level shift you may be falsely concluding about a persistent change over time. If your data is more representative of y(t) |
40,168 | How to intuitively understand formula for estimate of pooled variance when testing differences between group means? | There are really 2 questions here, one about pooling and one about degrees of freedom.
Let's look at degrees of freedom first. To get the concept consider if we know that $x+y+z=10$ Then $x$ can be anything we want, and $y$ can be anything we want, but once we set those 2 there is only one value that $z$ can be, so w... | How to intuitively understand formula for estimate of pooled variance when testing differences betw | There are really 2 questions here, one about pooling and one about degrees of freedom.
Let's look at degrees of freedom first. To get the concept consider if we know that $x+y+z=10$ Then $x$ can be | How to intuitively understand formula for estimate of pooled variance when testing differences between group means?
There are really 2 questions here, one about pooling and one about degrees of freedom.
Let's look at degrees of freedom first. To get the concept consider if we know that $x+y+z=10$ Then $x$ can be an... | How to intuitively understand formula for estimate of pooled variance when testing differences betw
There are really 2 questions here, one about pooling and one about degrees of freedom.
Let's look at degrees of freedom first. To get the concept consider if we know that $x+y+z=10$ Then $x$ can be |
40,169 | How to intuitively understand formula for estimate of pooled variance when testing differences between group means? | The pooled variance is a weighted average of the two independent unbiased estimators: $S^2_c$ and $S^2_t$.
Why those weights and what is the relation to the degrees of freedom?
Those weights are such that the weighted average is unbiased.
The degrees of freedom-
Accounting version: since you are summing differences... | How to intuitively understand formula for estimate of pooled variance when testing differences betw | The pooled variance is a weighted average of the two independent unbiased estimators: $S^2_c$ and $S^2_t$.
Why those weights and what is the relation to the degrees of freedom?
Those weights are such | How to intuitively understand formula for estimate of pooled variance when testing differences between group means?
The pooled variance is a weighted average of the two independent unbiased estimators: $S^2_c$ and $S^2_t$.
Why those weights and what is the relation to the degrees of freedom?
Those weights are such t... | How to intuitively understand formula for estimate of pooled variance when testing differences betw
The pooled variance is a weighted average of the two independent unbiased estimators: $S^2_c$ and $S^2_t$.
Why those weights and what is the relation to the degrees of freedom?
Those weights are such |
40,170 | Inner correlation of occurrences (burstiness?) in R | This is a widely studied problem in neurosciences, where you need to determine the "burstiness" of action potentials of a neuron. The methods, however, can be obviously applied to any series of events.
Most of them rely on the analysis of the intervals between two following events: in the case of action potentials thes... | Inner correlation of occurrences (burstiness?) in R | This is a widely studied problem in neurosciences, where you need to determine the "burstiness" of action potentials of a neuron. The methods, however, can be obviously applied to any series of events | Inner correlation of occurrences (burstiness?) in R
This is a widely studied problem in neurosciences, where you need to determine the "burstiness" of action potentials of a neuron. The methods, however, can be obviously applied to any series of events.
Most of them rely on the analysis of the intervals between two fol... | Inner correlation of occurrences (burstiness?) in R
This is a widely studied problem in neurosciences, where you need to determine the "burstiness" of action potentials of a neuron. The methods, however, can be obviously applied to any series of events |
40,171 | Measuring homogeneity across different spatial aggregations of data | There are many ways you can characterize homogeneity, so there could be many answers to your question. One of the most intuitive ways I have seen it displayed is in a book chapter, "Spatial Analysis of Regional Income Inequality" by Sergio Rey in the book Spatially Integrated Social Science (PDF). The approach Rey take... | Measuring homogeneity across different spatial aggregations of data | There are many ways you can characterize homogeneity, so there could be many answers to your question. One of the most intuitive ways I have seen it displayed is in a book chapter, "Spatial Analysis o | Measuring homogeneity across different spatial aggregations of data
There are many ways you can characterize homogeneity, so there could be many answers to your question. One of the most intuitive ways I have seen it displayed is in a book chapter, "Spatial Analysis of Regional Income Inequality" by Sergio Rey in the b... | Measuring homogeneity across different spatial aggregations of data
There are many ways you can characterize homogeneity, so there could be many answers to your question. One of the most intuitive ways I have seen it displayed is in a book chapter, "Spatial Analysis o |
40,172 | Measuring homogeneity across different spatial aggregations of data | Homogeneity Definition: To start, let's define homogeneity as the degree to which households grouped in same area are like one another for some attribute.
MAUP Approach
Paraphrasing the stated problem: We are uncertain how homogeneity changes as we decrease the spatial resolution of the design of how we group household... | Measuring homogeneity across different spatial aggregations of data | Homogeneity Definition: To start, let's define homogeneity as the degree to which households grouped in same area are like one another for some attribute.
MAUP Approach
Paraphrasing the stated problem | Measuring homogeneity across different spatial aggregations of data
Homogeneity Definition: To start, let's define homogeneity as the degree to which households grouped in same area are like one another for some attribute.
MAUP Approach
Paraphrasing the stated problem: We are uncertain how homogeneity changes as we dec... | Measuring homogeneity across different spatial aggregations of data
Homogeneity Definition: To start, let's define homogeneity as the degree to which households grouped in same area are like one another for some attribute.
MAUP Approach
Paraphrasing the stated problem |
40,173 | Using R's phyper to get the probability of list overlap | Trying to translate this into a statistical question, it seems you have a population with $a$ members and you take two random samples without replacement sized $b$ and $c$, and you want the distribution of $X$, the number appearing in both samples.
As an illustration, suppose $a=5$, $b=2$ and $c=3$. There are 100 ways ... | Using R's phyper to get the probability of list overlap | Trying to translate this into a statistical question, it seems you have a population with $a$ members and you take two random samples without replacement sized $b$ and $c$, and you want the distributi | Using R's phyper to get the probability of list overlap
Trying to translate this into a statistical question, it seems you have a population with $a$ members and you take two random samples without replacement sized $b$ and $c$, and you want the distribution of $X$, the number appearing in both samples.
As an illustrat... | Using R's phyper to get the probability of list overlap
Trying to translate this into a statistical question, it seems you have a population with $a$ members and you take two random samples without replacement sized $b$ and $c$, and you want the distributi |
40,174 | Most suitable distributions for modeling Monte Carlo Simulations | The books "Continuous univariate distributions" Vol 1 + Vol 2 by Johnson, Kotz and Balakrishnan (and there is a multivariate book too, I believe) are classical references, rich on the mathematical properties as well as giving examples of the usages of the different distributions they treat.
If you want details on a sp... | Most suitable distributions for modeling Monte Carlo Simulations | The books "Continuous univariate distributions" Vol 1 + Vol 2 by Johnson, Kotz and Balakrishnan (and there is a multivariate book too, I believe) are classical references, rich on the mathematical pro | Most suitable distributions for modeling Monte Carlo Simulations
The books "Continuous univariate distributions" Vol 1 + Vol 2 by Johnson, Kotz and Balakrishnan (and there is a multivariate book too, I believe) are classical references, rich on the mathematical properties as well as giving examples of the usages of the... | Most suitable distributions for modeling Monte Carlo Simulations
The books "Continuous univariate distributions" Vol 1 + Vol 2 by Johnson, Kotz and Balakrishnan (and there is a multivariate book too, I believe) are classical references, rich on the mathematical pro |
40,175 | Most suitable distributions for modeling Monte Carlo Simulations | I personally worry about the philosophy of deciding on a distribution with certain properties first and then defining the parameters for the distribution.
My advice is normally to get the best data that you can first and then to process that data to find the best distribution and parameters to fit it. If you decide a ... | Most suitable distributions for modeling Monte Carlo Simulations | I personally worry about the philosophy of deciding on a distribution with certain properties first and then defining the parameters for the distribution.
My advice is normally to get the best data t | Most suitable distributions for modeling Monte Carlo Simulations
I personally worry about the philosophy of deciding on a distribution with certain properties first and then defining the parameters for the distribution.
My advice is normally to get the best data that you can first and then to process that data to find... | Most suitable distributions for modeling Monte Carlo Simulations
I personally worry about the philosophy of deciding on a distribution with certain properties first and then defining the parameters for the distribution.
My advice is normally to get the best data t |
40,176 | Most suitable distributions for modeling Monte Carlo Simulations | I know I am resurrecting an old thread but I had recently been looking for similar information and was unable to find it on the forum. I eventually managed to implement a solution for my study and I will add my experiences on using Monte Carlo simulation.
Monte Carlo Simulations is a rather broad, all-encompassing term... | Most suitable distributions for modeling Monte Carlo Simulations | I know I am resurrecting an old thread but I had recently been looking for similar information and was unable to find it on the forum. I eventually managed to implement a solution for my study and I w | Most suitable distributions for modeling Monte Carlo Simulations
I know I am resurrecting an old thread but I had recently been looking for similar information and was unable to find it on the forum. I eventually managed to implement a solution for my study and I will add my experiences on using Monte Carlo simulation.... | Most suitable distributions for modeling Monte Carlo Simulations
I know I am resurrecting an old thread but I had recently been looking for similar information and was unable to find it on the forum. I eventually managed to implement a solution for my study and I w |
40,177 | Most suitable distributions for modeling Monte Carlo Simulations | If you know the domain of the random variable and maybe have knowledge of some other properties like the mean, variance, etc. but want to be ignorant in a fair way about all other aspects of the distribution, you can find a distribution by applying the principle of maximum entropy. Or put simply
Given a collection of ... | Most suitable distributions for modeling Monte Carlo Simulations | If you know the domain of the random variable and maybe have knowledge of some other properties like the mean, variance, etc. but want to be ignorant in a fair way about all other aspects of the distr | Most suitable distributions for modeling Monte Carlo Simulations
If you know the domain of the random variable and maybe have knowledge of some other properties like the mean, variance, etc. but want to be ignorant in a fair way about all other aspects of the distribution, you can find a distribution by applying the pr... | Most suitable distributions for modeling Monte Carlo Simulations
If you know the domain of the random variable and maybe have knowledge of some other properties like the mean, variance, etc. but want to be ignorant in a fair way about all other aspects of the distr |
40,178 | Number of eigenfunctions for kernel | The polynomial kernel $K(x,y) = (x \cdot y + 1)^d$ is easily represented in terms of monomials. The degree $d$ is the maximum degree of the polynomial computed by the kernel and therefore also the maximum degree of any contained monomial.
The problem of determining the number of monomials of degree exactly $d$ in $p$ i... | Number of eigenfunctions for kernel | The polynomial kernel $K(x,y) = (x \cdot y + 1)^d$ is easily represented in terms of monomials. The degree $d$ is the maximum degree of the polynomial computed by the kernel and therefore also the max | Number of eigenfunctions for kernel
The polynomial kernel $K(x,y) = (x \cdot y + 1)^d$ is easily represented in terms of monomials. The degree $d$ is the maximum degree of the polynomial computed by the kernel and therefore also the maximum degree of any contained monomial.
The problem of determining the number of mono... | Number of eigenfunctions for kernel
The polynomial kernel $K(x,y) = (x \cdot y + 1)^d$ is easily represented in terms of monomials. The degree $d$ is the maximum degree of the polynomial computed by the kernel and therefore also the max |
40,179 | Number of eigenfunctions for kernel | Take a simple example, if $x,z \in \mathbb{R}^2$, and $d=2$, then,
$$ (\left< x , z \right> + 1)^2 = (x_1z_1 + x_2z_2 + 1)^2 $$
$$ = x_1^2z_1^2 + x_2^2z_2^2 + 2x_1z_1x_2z_2 +x_1z_1 + x_2z_2 + 1$$
$$ = \left< (x_1^2, x_2^2, \sqrt{2}x_1x_2, x_1, x_2, 1), (z_1^2, z_2^2, \sqrt{2}z_1z_2, z_1, z_2, 1) \right> $$
$$ = \left< ... | Number of eigenfunctions for kernel | Take a simple example, if $x,z \in \mathbb{R}^2$, and $d=2$, then,
$$ (\left< x , z \right> + 1)^2 = (x_1z_1 + x_2z_2 + 1)^2 $$
$$ = x_1^2z_1^2 + x_2^2z_2^2 + 2x_1z_1x_2z_2 +x_1z_1 + x_2z_2 + 1$$
$$ = | Number of eigenfunctions for kernel
Take a simple example, if $x,z \in \mathbb{R}^2$, and $d=2$, then,
$$ (\left< x , z \right> + 1)^2 = (x_1z_1 + x_2z_2 + 1)^2 $$
$$ = x_1^2z_1^2 + x_2^2z_2^2 + 2x_1z_1x_2z_2 +x_1z_1 + x_2z_2 + 1$$
$$ = \left< (x_1^2, x_2^2, \sqrt{2}x_1x_2, x_1, x_2, 1), (z_1^2, z_2^2, \sqrt{2}z_1z_2, ... | Number of eigenfunctions for kernel
Take a simple example, if $x,z \in \mathbb{R}^2$, and $d=2$, then,
$$ (\left< x , z \right> + 1)^2 = (x_1z_1 + x_2z_2 + 1)^2 $$
$$ = x_1^2z_1^2 + x_2^2z_2^2 + 2x_1z_1x_2z_2 +x_1z_1 + x_2z_2 + 1$$
$$ = |
40,180 | Time series factor model with one series more frequent | The cannonical way is probably MIDAS regression. There is a Matlab toolbox for estimating, available upon request from the author Eric Ghysels. You might look into user guide of this toolbox, since it has a review of all literature on MIDAS.
The wikipedia page also talks about connection with Kalman filters, so @F. Tus... | Time series factor model with one series more frequent | The cannonical way is probably MIDAS regression. There is a Matlab toolbox for estimating, available upon request from the author Eric Ghysels. You might look into user guide of this toolbox, since it | Time series factor model with one series more frequent
The cannonical way is probably MIDAS regression. There is a Matlab toolbox for estimating, available upon request from the author Eric Ghysels. You might look into user guide of this toolbox, since it has a review of all literature on MIDAS.
The wikipedia page also... | Time series factor model with one series more frequent
The cannonical way is probably MIDAS regression. There is a Matlab toolbox for estimating, available upon request from the author Eric Ghysels. You might look into user guide of this toolbox, since it |
40,181 | Time series factor model with one series more frequent | I would cast the model in state-space form. Then there is no problem if one of the variables is observed more frequently than the other, or the observation times are irregular: the Kalman filter deals with missing and partially observed variables gracefully.
Without details on the exact kind of relationships you aim to... | Time series factor model with one series more frequent | I would cast the model in state-space form. Then there is no problem if one of the variables is observed more frequently than the other, or the observation times are irregular: the Kalman filter deals | Time series factor model with one series more frequent
I would cast the model in state-space form. Then there is no problem if one of the variables is observed more frequently than the other, or the observation times are irregular: the Kalman filter deals with missing and partially observed variables gracefully.
Withou... | Time series factor model with one series more frequent
I would cast the model in state-space form. Then there is no problem if one of the variables is observed more frequently than the other, or the observation times are irregular: the Kalman filter deals |
40,182 | How to update ARIMA forecast in R? [closed] | Update:
It turns out that the Arima function has an argument for supplying old model:
adj_s <- Arima(series,model=arima_s)
The end result might be the same for both approaches, but I would advise using second one, because it clearly is tested more thoroughly.
**Old answer: **
As it happens, I encountered similar prob... | How to update ARIMA forecast in R? [closed] | Update:
It turns out that the Arima function has an argument for supplying old model:
adj_s <- Arima(series,model=arima_s)
The end result might be the same for both approaches, but I would advise us | How to update ARIMA forecast in R? [closed]
Update:
It turns out that the Arima function has an argument for supplying old model:
adj_s <- Arima(series,model=arima_s)
The end result might be the same for both approaches, but I would advise using second one, because it clearly is tested more thoroughly.
**Old answer: ... | How to update ARIMA forecast in R? [closed]
Update:
It turns out that the Arima function has an argument for supplying old model:
adj_s <- Arima(series,model=arima_s)
The end result might be the same for both approaches, but I would advise us |
40,183 | R Package GBM - Bernoulli Deviance | It is a mathematical trick. We have
\begin{align*}
\log\frac{p_i}{1-p_i}=f(x_i)
\end{align*}
and from this we get
\begin{align*}
\frac{1}{1-p_i}=1+\exp(f(x_i))
\end{align*}
The log likelihood is
\begin{align*}
\sum_{i=1}^n\left[y_i\log(p_i)+(1-y_i)\log(1-p_i)\right]&=\sum_{i=1}^n\left[y_i\log\frac{p_i}{1-p_i}+\log(1-p... | R Package GBM - Bernoulli Deviance | It is a mathematical trick. We have
\begin{align*}
\log\frac{p_i}{1-p_i}=f(x_i)
\end{align*}
and from this we get
\begin{align*}
\frac{1}{1-p_i}=1+\exp(f(x_i))
\end{align*}
The log likelihood is
\beg | R Package GBM - Bernoulli Deviance
It is a mathematical trick. We have
\begin{align*}
\log\frac{p_i}{1-p_i}=f(x_i)
\end{align*}
and from this we get
\begin{align*}
\frac{1}{1-p_i}=1+\exp(f(x_i))
\end{align*}
The log likelihood is
\begin{align*}
\sum_{i=1}^n\left[y_i\log(p_i)+(1-y_i)\log(1-p_i)\right]&=\sum_{i=1}^n\lef... | R Package GBM - Bernoulli Deviance
It is a mathematical trick. We have
\begin{align*}
\log\frac{p_i}{1-p_i}=f(x_i)
\end{align*}
and from this we get
\begin{align*}
\frac{1}{1-p_i}=1+\exp(f(x_i))
\end{align*}
The log likelihood is
\beg |
40,184 | R Package GBM - Bernoulli Deviance | I'm also studying the GBM package!
mpiktas, i think you forgot a log on the left hand side in the 2nd equation? I assume you substitute p_i with 1/(1+exp(-f(x_i))), but then in the 2nd row above there is log(1/...) = log(1+...), or am i wrong? Anyway, i think then you did it right in the third row...
Can you tell us w... | R Package GBM - Bernoulli Deviance | I'm also studying the GBM package!
mpiktas, i think you forgot a log on the left hand side in the 2nd equation? I assume you substitute p_i with 1/(1+exp(-f(x_i))), but then in the 2nd row above ther | R Package GBM - Bernoulli Deviance
I'm also studying the GBM package!
mpiktas, i think you forgot a log on the left hand side in the 2nd equation? I assume you substitute p_i with 1/(1+exp(-f(x_i))), but then in the 2nd row above there is log(1/...) = log(1+...), or am i wrong? Anyway, i think then you did it right in... | R Package GBM - Bernoulli Deviance
I'm also studying the GBM package!
mpiktas, i think you forgot a log on the left hand side in the 2nd equation? I assume you substitute p_i with 1/(1+exp(-f(x_i))), but then in the 2nd row above ther |
40,185 | How to increase size of label fonts in barplot | According to ?barplot, you need to use cex.names=1.5.
barplot(mx, beside=TRUE, col=c("grey"), names.arg=results$"RUN",
cex.axis=1.5, cex.names=1.5) | How to increase size of label fonts in barplot | According to ?barplot, you need to use cex.names=1.5.
barplot(mx, beside=TRUE, col=c("grey"), names.arg=results$"RUN",
cex.axis=1.5, cex.names=1.5) | How to increase size of label fonts in barplot
According to ?barplot, you need to use cex.names=1.5.
barplot(mx, beside=TRUE, col=c("grey"), names.arg=results$"RUN",
cex.axis=1.5, cex.names=1.5) | How to increase size of label fonts in barplot
According to ?barplot, you need to use cex.names=1.5.
barplot(mx, beside=TRUE, col=c("grey"), names.arg=results$"RUN",
cex.axis=1.5, cex.names=1.5) |
40,186 | Recreating R's hist function's bin counting | Perhaps I've misunderstood what you want, but hist() returns all the details required to produce the histogram that is plotted. But you don't need to plot it and you can capture the returned object for subsequent use. So if the histogram contains the relevant summary you are after, this should be all you need. Here's a... | Recreating R's hist function's bin counting | Perhaps I've misunderstood what you want, but hist() returns all the details required to produce the histogram that is plotted. But you don't need to plot it and you can capture the returned object fo | Recreating R's hist function's bin counting
Perhaps I've misunderstood what you want, but hist() returns all the details required to produce the histogram that is plotted. But you don't need to plot it and you can capture the returned object for subsequent use. So if the histogram contains the relevant summary you are ... | Recreating R's hist function's bin counting
Perhaps I've misunderstood what you want, but hist() returns all the details required to produce the histogram that is plotted. But you don't need to plot it and you can capture the returned object fo |
40,187 | Quantitative methods and statistics conferences in psychology? | The European Association of Methodology has a meeting turning around statistics and psychometrics for applied research in social, educational and psychological science every two years. The latest was held in Postdam two months ago. | Quantitative methods and statistics conferences in psychology? | The European Association of Methodology has a meeting turning around statistics and psychometrics for applied research in social, educational and psychological science every two years. The latest was | Quantitative methods and statistics conferences in psychology?
The European Association of Methodology has a meeting turning around statistics and psychometrics for applied research in social, educational and psychological science every two years. The latest was held in Postdam two months ago. | Quantitative methods and statistics conferences in psychology?
The European Association of Methodology has a meeting turning around statistics and psychometrics for applied research in social, educational and psychological science every two years. The latest was |
40,188 | Quantitative methods and statistics conferences in psychology? | You're probably already aware of it, but the Society for Mathematical Psychology has an annual conference, MathPsych, which is attached to CogSci (generally happnens in the same city either before or after) and blends statistical methodology and Psychological modeling.
They do a pretty good job getting big names to com... | Quantitative methods and statistics conferences in psychology? | You're probably already aware of it, but the Society for Mathematical Psychology has an annual conference, MathPsych, which is attached to CogSci (generally happnens in the same city either before or | Quantitative methods and statistics conferences in psychology?
You're probably already aware of it, but the Society for Mathematical Psychology has an annual conference, MathPsych, which is attached to CogSci (generally happnens in the same city either before or after) and blends statistical methodology and Psychologic... | Quantitative methods and statistics conferences in psychology?
You're probably already aware of it, but the Society for Mathematical Psychology has an annual conference, MathPsych, which is attached to CogSci (generally happnens in the same city either before or |
40,189 | Quantitative methods and statistics conferences in psychology? | The annual meeting of the Society for Computers in Psychology often features content on quantitative methods:
http://sites.google.com/site/scipws/ | Quantitative methods and statistics conferences in psychology? | The annual meeting of the Society for Computers in Psychology often features content on quantitative methods:
http://sites.google.com/site/scipws/ | Quantitative methods and statistics conferences in psychology?
The annual meeting of the Society for Computers in Psychology often features content on quantitative methods:
http://sites.google.com/site/scipws/ | Quantitative methods and statistics conferences in psychology?
The annual meeting of the Society for Computers in Psychology often features content on quantitative methods:
http://sites.google.com/site/scipws/ |
40,190 | XGBoost: universal approximator? | Yes, it makes sense to characterise GBMs as "universal function approximators" and in particular "greedy" ones, as put forward in Friedman's (2001) (uber-classic) Greedy function approximation: A gradient boosting machine. The greediness here stemming on how we gradually/stage-wise increase the ensemble's capacity by ... | XGBoost: universal approximator? | Yes, it makes sense to characterise GBMs as "universal function approximators" and in particular "greedy" ones, as put forward in Friedman's (2001) (uber-classic) Greedy function approximation: A gra | XGBoost: universal approximator?
Yes, it makes sense to characterise GBMs as "universal function approximators" and in particular "greedy" ones, as put forward in Friedman's (2001) (uber-classic) Greedy function approximation: A gradient boosting machine. The greediness here stemming on how we gradually/stage-wise inc... | XGBoost: universal approximator?
Yes, it makes sense to characterise GBMs as "universal function approximators" and in particular "greedy" ones, as put forward in Friedman's (2001) (uber-classic) Greedy function approximation: A gra |
40,191 | Logistic regression with known probabilities for some datapoints | I will assume that the objective here is to fit the model subject to the constraint that the predicted probability for one of the observations should have a value of exactly $p_0$ (not just a value "around" $p_0$).
Letting $\mathbf{x}_0$ denote the covariate vector for that observation, this amounts to a linear constra... | Logistic regression with known probabilities for some datapoints | I will assume that the objective here is to fit the model subject to the constraint that the predicted probability for one of the observations should have a value of exactly $p_0$ (not just a value "a | Logistic regression with known probabilities for some datapoints
I will assume that the objective here is to fit the model subject to the constraint that the predicted probability for one of the observations should have a value of exactly $p_0$ (not just a value "around" $p_0$).
Letting $\mathbf{x}_0$ denote the covari... | Logistic regression with known probabilities for some datapoints
I will assume that the objective here is to fit the model subject to the constraint that the predicted probability for one of the observations should have a value of exactly $p_0$ (not just a value "a |
40,192 | Logistic regression with known probabilities for some datapoints | One way you can do this that doesn't involve customized loss functions is by augmenting your data with "fake data" that reflects your prior beliefs about the data points. The augmented data consists of some data points with the same values for the features as the data points for which you have prior beliefs and, in th... | Logistic regression with known probabilities for some datapoints | One way you can do this that doesn't involve customized loss functions is by augmenting your data with "fake data" that reflects your prior beliefs about the data points. The augmented data consists | Logistic regression with known probabilities for some datapoints
One way you can do this that doesn't involve customized loss functions is by augmenting your data with "fake data" that reflects your prior beliefs about the data points. The augmented data consists of some data points with the same values for the featur... | Logistic regression with known probabilities for some datapoints
One way you can do this that doesn't involve customized loss functions is by augmenting your data with "fake data" that reflects your prior beliefs about the data points. The augmented data consists |
40,193 | Hypothesis testing of two dependent samples when pair information is not given | If you only have information about each sample separately and nothing about either the pair-differences or say the pair-correlation (nor covariance, nor any other relevant information), you don't have the ability to work out $s_d$. If you assume that the dependence is non-negative (an uncontroversial assumption in most... | Hypothesis testing of two dependent samples when pair information is not given | If you only have information about each sample separately and nothing about either the pair-differences or say the pair-correlation (nor covariance, nor any other relevant information), you don't have | Hypothesis testing of two dependent samples when pair information is not given
If you only have information about each sample separately and nothing about either the pair-differences or say the pair-correlation (nor covariance, nor any other relevant information), you don't have the ability to work out $s_d$. If you as... | Hypothesis testing of two dependent samples when pair information is not given
If you only have information about each sample separately and nothing about either the pair-differences or say the pair-correlation (nor covariance, nor any other relevant information), you don't have |
40,194 | Implementing a VAE in pytorch - extremely negative training loss | The problem is return -MSE_loss + KLDiv_Loss. You don't want to minimize -MSE_loss because you can always make $-(x-c)^2$ smaller by choosing $x$ farther from $c$. If $c$ is your target, this means your model is getting further from your goal.
Use return MSE_loss + KLDiv_Loss instead. You can show that this is correct ... | Implementing a VAE in pytorch - extremely negative training loss | The problem is return -MSE_loss + KLDiv_Loss. You don't want to minimize -MSE_loss because you can always make $-(x-c)^2$ smaller by choosing $x$ farther from $c$. If $c$ is your target, this means yo | Implementing a VAE in pytorch - extremely negative training loss
The problem is return -MSE_loss + KLDiv_Loss. You don't want to minimize -MSE_loss because you can always make $-(x-c)^2$ smaller by choosing $x$ farther from $c$. If $c$ is your target, this means your model is getting further from your goal.
Use return ... | Implementing a VAE in pytorch - extremely negative training loss
The problem is return -MSE_loss + KLDiv_Loss. You don't want to minimize -MSE_loss because you can always make $-(x-c)^2$ smaller by choosing $x$ farther from $c$. If $c$ is your target, this means yo |
40,195 | How can we know population mean but not variance | $\mathbf{\mu}$ is the theorized value under the null hypothesis.
For the situation in general:
$$
H_0:\mu = \mu_0\\
H_a:\mu\ne\mu_0
$$
We do our usual fun of calculating the sample mean $\bar x$ and sample variance $s^2$, and then we calculate the t-statistic.
$$
t = \dfrac{\bar x - \mu_0}{\sqrt{s^2/n}}
$$
Now let's do... | How can we know population mean but not variance | $\mathbf{\mu}$ is the theorized value under the null hypothesis.
For the situation in general:
$$
H_0:\mu = \mu_0\\
H_a:\mu\ne\mu_0
$$
We do our usual fun of calculating the sample mean $\bar x$ and s | How can we know population mean but not variance
$\mathbf{\mu}$ is the theorized value under the null hypothesis.
For the situation in general:
$$
H_0:\mu = \mu_0\\
H_a:\mu\ne\mu_0
$$
We do our usual fun of calculating the sample mean $\bar x$ and sample variance $s^2$, and then we calculate the t-statistic.
$$
t = \df... | How can we know population mean but not variance
$\mathbf{\mu}$ is the theorized value under the null hypothesis.
For the situation in general:
$$
H_0:\mu = \mu_0\\
H_a:\mu\ne\mu_0
$$
We do our usual fun of calculating the sample mean $\bar x$ and s |
40,196 | How can we know population mean but not variance | This isn't generally applicable, but here's a specific example where the mean is knowable.
Suppose the population is "individuals' net profits in poker games over the last 6 months".
Poker doesn't create or destroy money, so if you add up everybody's winnings and losses the total will be zero. (Let's assume that we are... | How can we know population mean but not variance | This isn't generally applicable, but here's a specific example where the mean is knowable.
Suppose the population is "individuals' net profits in poker games over the last 6 months".
Poker doesn't cre | How can we know population mean but not variance
This isn't generally applicable, but here's a specific example where the mean is knowable.
Suppose the population is "individuals' net profits in poker games over the last 6 months".
Poker doesn't create or destroy money, so if you add up everybody's winnings and losses ... | How can we know population mean but not variance
This isn't generally applicable, but here's a specific example where the mean is knowable.
Suppose the population is "individuals' net profits in poker games over the last 6 months".
Poker doesn't cre |
40,197 | Problem of computing standard error of higher moments | Here is a quick check using the moments of moments functions from the mathStatica package for Mathematica. That uses power sum notation where $s_r = \sum_{i=1}^n X_i^r$. Your problem is to find $E[O_2 O_1^2]$, which denotes the 1st RawMoment of $\frac{s_2}{n} \frac{s_1^2} {n^2}$:
where the solution is expressed in te... | Problem of computing standard error of higher moments | Here is a quick check using the moments of moments functions from the mathStatica package for Mathematica. That uses power sum notation where $s_r = \sum_{i=1}^n X_i^r$. Your problem is to find $E[O_ | Problem of computing standard error of higher moments
Here is a quick check using the moments of moments functions from the mathStatica package for Mathematica. That uses power sum notation where $s_r = \sum_{i=1}^n X_i^r$. Your problem is to find $E[O_2 O_1^2]$, which denotes the 1st RawMoment of $\frac{s_2}{n} \frac... | Problem of computing standard error of higher moments
Here is a quick check using the moments of moments functions from the mathStatica package for Mathematica. That uses power sum notation where $s_r = \sum_{i=1}^n X_i^r$. Your problem is to find $E[O_ |
40,198 | Estimating the Population Mean with the Sample Mean | Let $X_{1},X_{2},\dots ,X_{n}$ are $n$ random samples drawn from a population with overall mean $\mu$ and finite variance $\sigma ^{2}$ and if $\bar {X}_{n}$ is the sample mean, then
We know sample mean (statistic) is an unbiased estimator of the population mean (parameter) i.e., $E[\bar{X_n}]=\mu$
By SLLN we have $\... | Estimating the Population Mean with the Sample Mean | Let $X_{1},X_{2},\dots ,X_{n}$ are $n$ random samples drawn from a population with overall mean $\mu$ and finite variance $\sigma ^{2}$ and if $\bar {X}_{n}$ is the sample mean, then
We know sample | Estimating the Population Mean with the Sample Mean
Let $X_{1},X_{2},\dots ,X_{n}$ are $n$ random samples drawn from a population with overall mean $\mu$ and finite variance $\sigma ^{2}$ and if $\bar {X}_{n}$ is the sample mean, then
We know sample mean (statistic) is an unbiased estimator of the population mean (pa... | Estimating the Population Mean with the Sample Mean
Let $X_{1},X_{2},\dots ,X_{n}$ are $n$ random samples drawn from a population with overall mean $\mu$ and finite variance $\sigma ^{2}$ and if $\bar {X}_{n}$ is the sample mean, then
We know sample |
40,199 | Estimating the Population Mean with the Sample Mean | If you have a sample $X_1, X_2, \dots, X_n$ from a normal population in vector x, the procedure t.test in R will give you a 95% confidence
interval $(49.19, 50.33)$ for the population mean $\mu,$ for a sample of size $n = 20$ from $\mathsf{Norm}(\mu = 50, \sigma=7),$ as shown below.
(No hand computation is needed.)
set... | Estimating the Population Mean with the Sample Mean | If you have a sample $X_1, X_2, \dots, X_n$ from a normal population in vector x, the procedure t.test in R will give you a 95% confidence
interval $(49.19, 50.33)$ for the population mean $\mu,$ for | Estimating the Population Mean with the Sample Mean
If you have a sample $X_1, X_2, \dots, X_n$ from a normal population in vector x, the procedure t.test in R will give you a 95% confidence
interval $(49.19, 50.33)$ for the population mean $\mu,$ for a sample of size $n = 20$ from $\mathsf{Norm}(\mu = 50, \sigma=7),$ ... | Estimating the Population Mean with the Sample Mean
If you have a sample $X_1, X_2, \dots, X_n$ from a normal population in vector x, the procedure t.test in R will give you a 95% confidence
interval $(49.19, 50.33)$ for the population mean $\mu,$ for |
40,200 | What are the simplest examples of nonlinear statistical functionals? | Variances. Per the Wikipedia page on mixture distributions, expectations are linear, but variances are not. (When you think about it, this is kind of obvious, because expectations involve integrating over a function, which is linear, but variances involve integrating over the square of a function, which isn't.)
Specifi... | What are the simplest examples of nonlinear statistical functionals? | Variances. Per the Wikipedia page on mixture distributions, expectations are linear, but variances are not. (When you think about it, this is kind of obvious, because expectations involve integrating | What are the simplest examples of nonlinear statistical functionals?
Variances. Per the Wikipedia page on mixture distributions, expectations are linear, but variances are not. (When you think about it, this is kind of obvious, because expectations involve integrating over a function, which is linear, but variances inv... | What are the simplest examples of nonlinear statistical functionals?
Variances. Per the Wikipedia page on mixture distributions, expectations are linear, but variances are not. (When you think about it, this is kind of obvious, because expectations involve integrating |
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