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I am working with a data set having N around 200,000. In regressions, I am seeing very small significance values << 0.001 associated with very small effect sizes, e.g. r=0.028. What I'd like to know is, is there a principled way of deciding an appropriate significance threshold in relation to the sample size? Are ther... |
I am writing a hierarchical BUGS model that involves both linear and angle variables. I want the hyper-parameters to be normally distributed, which is straight-forward for the linear variables, but I'm not sure what to do about the angle variables. I would gladly use a von Mises distribution or a truncated normal, bu... |
I know that the pdf of a power law distribution is $$ p(x) = \frac{\alpha-1}{x_{\text{min}}} \left(\frac{x}{x_{\text{min}}} \right)^{-\alpha}$$
But what does it intuitively mean if, for example, stock prices follow a power law distribution? Does this mean that losses can be very high but infrequent?
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I am looking for some medium to long length papers/websites/etc about data mining, specifically where one dataset is explored in depth from data preparation through final model. I am particularly interested in discussions about application of machine learning algos and also basic data modeling. An example would be Lu... |
I have a question on the term used for the type of the item that I would like to use and how it is "usually" analysed.
Let's assume that we have two items and the one is linked to the other, namely, the one is the answer in a multiple choice question and the second on quantifies an attribute, for example, "did you fi... |
I've subsetted and plotted my unweighted data, but I don't see how to make use of my weighting variable "FactBx$expwgt"
#####################################################
plot( density(FactB1$BAV_DIST),
#### xlim=range( c(FactB1$BAV_DIST, FactB2$BAV_DIST, FactB3$BAV_DIST) ),
xlim = c(0, 10),
main = "Den... |
I have an algorithm that take as input some data (that are continuously arriving) and 3 or 4 parameter values that should be specified by the user. At the and of execution (or periodically during execution) we can evaluate the performance to see if the chosen parameter values were good or not, but the evaluation functi... |
Say, I have a biomarker that is strongly associated to a gene. This biomarker is also strongly associated to another trait, like glucose, but the gene is not.
If I perform a regression between the glucose and biomarker + gene I get the biomaker and gene both significant: Is this a spurious effect?
And what if I add ... |
I'm looking for good panel data analysis references. I've Baltagi & "Panel Data Econometrics in R". But I want something which is short (max 100 pgs), lucid (know this criteria differs) & have examples to work through.
I'll be grateful if somebody can give me some references.
|
If $\text{RMSEA} = 0$, it means $\chi^2 < df$.
Does it disqualify RMSEA as a criterion to evaluate the model fit, or is it just the explanation why it is zero?
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I'm looking for a reference for the Naive bayse classifier to put in my work. Not sure what I'm missing but a scholar search didn't yield any meaningful results. any idea ?
update: I meant for a reference to put in the bibliography...
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The qqnorm() R function produces a normal QQ-plot and qqline() adds a line which passes through the first and third quartiles. What is the origin of this line? Is it helpful to check normality? This is not the classical line (the diagonal $y=x$ possibly after linear scaling).
Here is an example. First I compare the emp... |
I am using the R programming language to analyze the cointegration of 5 data sets. I have determined that the series share 3 cointegrating vectors after taking structural shifts into account following the approach of Lutkepohl et al (2004).
My questions are:
How can I incorporate the 3 vectors into the full matrix for... |
I have the following linear model with 2 predictors $x_1, x_2$ and a cubic transform on $x_1$:
$$
\hat{Y} = c +\beta_1x_1 + \beta_2x_1^2 + \beta_3x_1^3 + \beta_4x_2
$$
Where $x_1$ can assume negative values. I ran several regressions on subsets of the original population based on the absolute value of $x_1$ and notic... |
I have what I hope should be a relatively quick and easy to answer question.
Would it ever make sense to replace the probabilities by an objective function within the relative entropy formulation?
I imagine a form of robust decision making under which a decision maker wishes to choose some value $s$ that maximises some... |
I have a set of numbers (number of publications for each year of one person). I don't know the exact distribution. Now I also have another set of numbers to compare that to (number of publications for each year of the same person during another time period).
I'm interested whether the person has a higher publishing rat... |
The mcmcsamp() function generates simulations from the posterior distributions of a Bayesian mixed model fitted with the lmer() function. What are the prior distributions? Does there exist a book or an article providing a mathematical study of the Bayesian mixed model with these priors?
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I understand why the mean of the input to a neural network is normalized, to avoid the numerical problems with very large and very small numbers. Also, it's nice if the bias node is around the same magnitude as the other input data.
But why is the standard deviation usually normalized? Of course higher standard deviati... |
Suppose one wants to assess the goodness of fit between a theoretical distribution and an empirical distribution in graphical manner. The normal Q-Q plot is good if the theoretical distribution is normal. But what if the theoretical distribution is not normal? Can you use a double logarithmic graphical method?
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This is a technical problem I encountered in research. Sorry if it reads simple for professional statisticians.
Let $X_1, \cdots, X_n$ be iid standard Cauchy random variables. What estimate or exact formula exists for the median of $\sum_{i=1}^n |X_i|$? For $n=1$, it can be worked out explicitly the median is $1$:
$$
... |
Here's a probability question (probably really simple) I'm not sure how to solve:
Gamma distribution $X\sim \mathcal{G}(\alpha,\beta)$ with $\mu = 20$ and $\sigma^2 = 80$
$P(X \le 24)$ = ?
The previous question was finding the values of $\alpha$ and $\beta$, which I did using $\mu$ = $\alpha$$\beta$ and $\sigma^2$ = $\... |
In simple linear regression, $t = \frac{\hat\beta_1 - \beta_1}{\hat\sigma \sqrt{S_{xx}}} $ is the test statistic for the null hypothesis $H_0 : \beta_1 = 0$. How can I express $t^2$ as an F-distribution, i.e. as the ratio of two independent chi-squared random variables divided by their respective degrees of freedom?
So... |
This is an assignment question, but I am not interested in the answer.
This is the question:
A student is preparing for an upcoming exam. The professor for the course has given the class 30 questions to study from and plans to select 10 of the questions for use on the actual exam. Suppose that the student knows how to... |
I wrote this question which was previously answered
I am exposing several experimental organisms to a toxin in the food.
If the dosage is high enough, they will die. I am examining the number
that are dead after a day. For practical reasons, the dosage they get
is not directly under my control. I have decided to... |
I have a large training set, and it is too big to apply some algorithms due to computation limits.
What are the common methods to decrease training set size without losing significant amount of information?
Edit:
Training examples have 3 features and it is a 0/1 classification task.
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I'm not a statistician. So, please bear with my blunders if any.
Would you please explain in a simple manner how simulation is done? I know that it picks some random sample from a normal distribution and use for simulating. But, don't understand clearly.
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OK, I am not a statistician (Not even close). I am a High Performance Computing researcher and I wanted a few test cases for Large (Greater than 5000x5000) Dense Matrices. I had asked here and a few other places but never got any reply from a statistician. I am very much interested in trying out my codes on a statistic... |
B"H
Hello,
Assume I have a very large set of vectors ($X_i$) over some feature space ($F_i$), each vector is labeled as either $+1$ or $-1$. For convenience lets refer to this set as "the history set".
THE QUESTION:
Given a new vector $X_{test}$ to be classified (as either "+1" or "-1"), I'd like to find the history se... |
I understand deriving a covariance matrix from phylogenetic data to make $cov(X,Y) = 0$ for two variables you're making a regression on. But what happens if you have one continuous variable, that you've previously shown to be dependent on phylogeny, and one ordinal variable? The latter being ordinal, I'm not sure how t... |
Let's say I have to make a multiple regression like:
$ Y_i = \beta_0 + \beta_1 x_i + \beta_2 w_i + ... +\beta_3 z_i + \epsilon_i $
Then I run a Ramsey RESET test upon it and discover that my linear specification is not good. What is the best way to cope with non-linearity? I know that I could specify a log-log model, a... |
Given n random variables x1,...,xn (one-dimensional).
The following is known (corr() = Pearson correlation):
corr(x1,x2) = a
corr(x2,x3) = a
The actual values of the random variables are unkown though. Only some of their correlations are known.
From this, is it possible to calculate
corr(x3,x1) = ?
Or more generally... |
I have a data set that is imbalanced and would like to weight the samples to compensate, however I can't find code to implement this in R though I believe there is a feature in the randomForest package to do this.
Here's a sample dataset :
id buy=1/noBuy=0 timeOnSite(sec.) clicksOnSite estAge
1 ... |
I'm trying to simulate this scenario: 10 different algorithms are solving a number of problems. All 10 are run on each problem instance, which means if a particular problem instance is hard, I expect all algorithms to perform quite bad and vice versa.
Now, to simulate this, I created 10 different normal distributions ... |
I want to fit a fairly "standard" Poisson model, but with an autoregressive term.
$N_i \sim \mathrm{Pois}( \lambda_i E_i)$
with $\log \lambda_i = X_i \beta + \delta$
$\delta \sim AR(1)$
$X_i$ is a vector of covariates.
$\beta$ are my coefficients.
$\delta$ is an autoregressive term.
$E_i$ is the size of population at t... |
In my free time, I am working on a small web based system which collects crash reports (but not other, non-crashing bug reports) that are sent from Delphi Windows applications.
For troubleshooting, users would love to have a data-mining feature to find relationships between hardware or operating system versions and the... |
I am building a predictive model that forecasts a student's probability of success at the end of a term. I’m specifically interested in whether the student succeeds or fails, where success is usually defined as completing the course and achieving 70% or more points out of the total points possible.
When I deploy the mo... |
There are various definitions of fixed and random effects in linear mixed-effects models in the statistics literature, and even in the fixed-effects models from econometrics. The data I have now represent the entire consumption of some products in a country. I aggregated them to annual level for 6 years by each state. ... |
In R, given a set of numerical data, how do I decide the top 1% of these data, and replace them with a new value, say, e.g. $10^{10}$
|
I am conducting a study, in which I will be using Logistic Regression for analysis. I will be collecting data in an education setting (about cancer prevention), then I will follow subjects to collect data on who took action after their education (cancer prevention screening) among those who were educated. The two group... |
I keep reading about instances where we center the data (e.g., with regularization or PCA) in order to remove the intercept (as mentioned in this question). I know it's simple, but I'm having a hard time intuitively understanding this. Could someone provide the intuition or a reference I can read?
|
Let's say I have two numerical features were is suspect that the ratio between them is the most meaningful way of looking at them. I have a NN learner.
Should I add the ratio as a third feature or is this just idiotic?
In other words: Would it make sense to do a feature selection with all three of them and see if one o... |
Could someone explain to me:
the concept of "conditioning" in spatial statistics in a fairly advanced context?
Here is an example to clarify the question:
Step 1) generate a 2D point process, here 6 realization are shown:
Step 2) choose a region from one realization
Step 3) try to fill the rest of same size region, h... |
It is a common practice to measure an SVM model's performance by calculating its MSE (Mean Square Error). Why not use Mean Absolute Error (averaging errors' absolute values instead of squared values)?
|
I know there're at least two approaches for nonparametric curve estimation: kernel and orthogonal basis.
What are their advantages and disadvantages over each other?
And what are the typical application scenarios of each approach?
Could anyone give a hopefully not-too-long explanation?
|
How can one obtain standardized (fixed effect) regression weights from a multilevel regression?
And, as an "add-on": What is the easiest way to obtain these standardized weights from a mer-object (from the lmer function of the lme4package in R)?
|
A published article (pdf) contains these 2 sentences:
Moreover, misreporting may be caused by the application of incorrect rules or by a lack of knowledge of the statistical test. For example, the total df in an ANOVA may be taken to be the error df in the reporting of an $F$ test, or the researcher may divide the rep... |
This CrossValidated post on estimating mean from uneven samples suggests using a (any) Riemann Sum to estimate the integral of an unevenly sampled 1D function.
To me the obvious extension to 2D is this: given an uneven sample of f(x,y), create a triangulation of your sample points (x_i,y_i,z_i). Estimate the integr... |
I would like to perform wavelet smoothing at different scales in R. I got the idea from this figure (panel A) were they measure the density/intensity of a particular signal at different scales.
Here the image shows the linear coordinates along one chromosome on the x axis and the y axis shows the 'bandwidth' or bin siz... |
I have implemented a genetic algorithm for a fuzzy c-means clustering in Matlab. Its performance should be apriori better than that of the classic fuzzy c-means (fcm function in matlab). However, on all the classical datasets I am comparing them, both algorithms converge to the same value of the objective function.
My ... |
I asked this question on Stack Exchange, but I think it might be too specialized. Hopefully someone in the mixed model group can help me out.
I want to be able to bootstrap the variance differences between two data sets obtained at different times while taking out the error in a random effect.
I have 2 sets of experim... |
If my data has different response categories (i.e. a 3-category vs. 4-category ordinal data), can I use standard factor analysis procedures? Do I need to transform my data in any way before starting analysis?
Thank you!
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Suppose we have a variable $x$ with 3 levels (e.g. $x_1, x_2$ and $x_3$). We want to see if there is an interaction between $x$ and $y$ where $y$ is a continuous variable. To test this would we include the following terms: $x_{1}y, x_{2}y, x_{3}y$ in a regression model?
|
I have a question:
Is the ROC curve enough to assess goodness of fit? Or do you need other tests?
|
I have two predictors (X1 and X2) with large Beta's and large predictor-independent variable correlations (say it is like rs). In addition, I have another variable (X3; Beta=0.17; rs=0.18) which was not statistically significant in predicting the scores in the dependent variable. Last, I have an X4 with zero Beta and z... |
Some material I've seen on machine learning said that it's a bad idea to approach a classification problem through regression. But I think it's always possible to do a continuous regression to fit the data and truncate the continuous prediction to yield discrete classifications. So why is it a bad idea?
|
Given the regression model
$y=\beta_0 + 300*ln(x_1) - 15 * (ln(x_1))^2 + \beta_3*x_2 + ... + u $
$x$ ranges from 1 000 to 30 000 (6.9 to 10.3 on a logarithmic scale). How to interpret the diminishing effect that $x_1$ has on $y$? Is it possible to interpret it for small changes in $x_1$ analog to a "normal" quadratic t... |
Quoting from a Wikipedia article on parameter estimation for a naive Bayes classifier: "a typical assumption is that the continuous values associated with each class are distributed according to a Gaussian distribution."
I understand that a Gaussian distribution is convenient for analytical reasons. However, is there a... |
I need to analyse a dataset of clinical rehabilitation data. I am interested in hypothesis-driven relationships between quantified "input" (amount of therapy) and changes in health status. Although the dataset is relatively small (n~70) we have repeated data reflecting temporal changes in both. I am familiar with non-l... |
In the first chapter of the book Algebraic Geometry and Statistical Learning Theory which talks about the convergence of estimations in different functional space, it mentions that the Bayesian estimation corresponds to the Schwartz distribution topology, whereas the maximum likelihood estimation corresponds to the sup... |
I am learning logistic regression modeling using the book "Applied Logistic Regression" by Hosmer.
In chpaters, he suggested using Fractional Polynomials for fitting continuous variable which does not seems to be related to logit in linear fashion. I tried the mfp package and can give exactly the same verbose as the bo... |
I have a large data set consisting of the values of several hundred financial variables that could be used in a multiple regression to predict the behavior of an index fund over time. I would like to reduce the number of variables to ten or so while still retaining as much predictive power as possible. Added: The red... |
Possible Duplicate:
Sample size for a variable number of answers
I have data which was generated from a (theoretical) multinomial distribution, and using the data points I estimate the multinomial probabilities. Ideally, I would like to know how many points i would need be able to be confident that the inferred mult... |
I have faced a statistical problem in a computational biology question. I will explain it in statistical language.
Let's say we have the results of rolling one red die, $x$, and $m$ black dice $y_1,\ldots,y_m$. These two types of dice are physical identical and both have $N$ faces. The question is finding the average e... |
Let $g(x)=a+bx$ and let $x$ have known distribution $f(x)$, say.
Is it possible to calculate:
$$\mathbb{E}[\log(g(x))] = \int f(x) \log{g(x)}\,\mathrm{d}x= \int \log(a + b x) f(x) \,\mathrm{d}x?$$
Many thanks for all of the help in advance.
|
I am looking for a program (in R or SAS or standalone, if free or low cost) that will do power analysis for ordinal logistic regression.
|
For predictive modeling, do we need to concern ourselves with statistical concepts such as random effects and non independence of observations (repeated measures)? For example....
I have data from 5 direct mail campaigns (occurred over the course of a year) with various attributes and a flag for purchase. Ideally, I wo... |
I have performed meta-analysis and included four different tests for publication bias in the analysis - classic fail-safe N, Orwin's fail-safe N, Duval and Tweedie's trim and fill as well as funnel plots. The tests did not seem to indicate publication bias in my material - according to classic fail-safe N 23 papers wou... |
Have a quick question about parameter selection for an SVM. I'm using a rbf kernel, so trying to optimize C and gamma. I have an example set of around 4500, about 700 features, and using 700 examples from the set for testing. My dataset does consist of time series. I've been using a 5 fold cross validation with a grid ... |
I'm a phd student in Geography, i need some help (or good ressources) to understand why and when i need to use PIT (Probability integral transform) in my validation program for simulation.
I explain the context : Actually, i'm running a lot of stochastic simulations (more than 100000) because i use genetic algorithm to... |
This question is about finding valid instrumental variables.
Variables are:
the response variable for the structural equation, $y$
the jointly endogenous RHS variable in the structural equation, $m$
two control variables ($x_1$ and $x_2$) that will always appear on the RHS of the structural equation
four potential ins... |
I am an economics student just starting out with R, and while I'm beginning to be somewhat comfortable with it, I also realize I need to strongly brush up my basic statistics.
I have individual level admin data (in the form of a rotating panel) documenting a reform which was implemented as a pilot project in some regi... |
I am attending a data analysis class and some of my well-rooted ideas are being shaken. Namely, the idea that the error (epsilon), as well as any other sort of variance, applies only (so I thought) to a group (a sample or whole population). Now, we're being taught that one of the regression assumptions is that the vari... |
Given a random number generator to produce random variables with a probability density function $f(x)$, how to generate random variables with probability density function $g(x)?$
|
The Elements of Statistical Learning says on page 113:
Sphere the data with respect to the common covariance estimates $\hat{\Sigma}$:
$X^* \leftarrow D^{-1/2}U^TX$ where $\hat{\Sigma} = UDU^T$. The common covariance estimate of $X^*$ will now be the identity.
Can someone help me understand why $X^* \leftarrow D^{... |
I have been working on fitting Cox model for prediction by using the rms package. I want to measure model calibration and discrimination.
Discrimination was measured by using rms::validate(); Dxy can be transferred to @Frank Harrell's $c$ index. But in this way, I cannot get 95% CI for the $c$ index. How can I do this ... |
I am using the penalized package in r for fitting a Cox model with a lasso penalty.
Does the Cox model + lasso need to test proportionality?
How do you get the p-value for the nonzero coefficients in the model?
Thanks for any response.
|
I am working on fitting a Cox model to predict. But several predictors violated the proportional hazards assumption. I am gonna to do stratified Cox model to adjust them. But the results of stratified Cox model would not include any information or influence of those stratified predictors. As I really want to see the in... |
I've been reading Tukey's book "Exploratory Data Analysis". Being written in 1977, the book emphasizes paper/pencil methods. Is there a more 'modern' successor which takes into account that we can now instantaneosly plot large data sets?
|
Given an experiment where a number of entities are measured/evaluated using two different methods in two different runs, how would one go about calculating the correlation between the runs?
In other words; I have about a thousand entities $X_i$ each with two scores $S_{i,1}$ and $S_{i,2}$, and two such datasets from t... |
Incremental clustering algorithms
Online clustering algorithms
Data stream clustering algorithms
Single pass clustering algorithms
Are the following expressions related? Does some of them include others? What is the difference between them? What are the constraints that each one should face unlike others?
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I am trying to find a solution to compare two "goodness-of-fit chi-square" tests.
More precisely, I want to compare results from two independent experiments. In these experiments the authors used the goodness-of-fit chi-square to compare random guessing (expected frequencies) with observed frequencies. The two experime... |
I want to choose variables for building a logistic regression model by comparing the GOF of different types. The problem is that some of the candidates have some missing values so I can´t use the likelihood ratio test for comparing models, given that when I want to compare models with those variables they are not neste... |
We draw $n$ values, each equiprobably among $m$ distinct values. What are the odds $p(n,m,k)$ that at least one of the values is drawn at least $k$ times? e.g. for $n=3000$, $m=300$, $k=20$.
Note: I was passed a variant of this by a friend asking for "a statistical package usable for similar problems".
My attempt: The ... |
What fields of science (social or physical) use modelling for time-series analysis? In particular, time-series that cannot be replicated? Two examples that I can think of are climate modelling, and econometrics. Are there any others?
I am asking because I am doing research on model independence in climate science, and ... |
I'm wondering what's the usual way for detecting "three-parts" association between variables. I'm not even sure about the name, so I apologize for any misunderstandings beforehand. Here is what I want. I suspect that buried behind some noise is a relation of this kind:
$$s=\begin{cases}
g(b) & \textrm{ if }f(a)\\
h(... |
I have been wondering, why are LASSO and LARS model selection methods so popular even though they are basically just variations of step-wise forward selection (and thus suffer from path dependency)?
Similarly, why are General to Specific (GETS) methods for model selection mostly ignored, even though they do better tha... |
Let's say that I am putting together a logistic regression model where I am predicting
something (y) based on the day of the week. However, the model needs to account for each single day.
Therefore, instead of:
y = B0 + B1*(day)
Where day is a categorical variable with 7 levels.
It would be:
y = B0 + B1*(monday) + B2... |
I'm analyzing an experiment results. The experiment (unfortunately) was very bad designed .. anyhow this is what I have ....
In this experiment two group of patients: healthy control and with X disease. The sick people came for three visits: during the first visit they took no medication at all. During the second meet... |
Can I understand factor analysis in the following way?
Assume I have 5 independent variables (A,B,C,D,E)
Factor analysis allows me to make (D,E) to be dependent variables and allow me to make them to be linear combinations of (A,B,C).
Therefore I will only need to carry (A,B,C) data and the $\Lambda$ matrix, then I can... |
Suppose we ran a simple linear regression $y=\beta_0+\beta_1x+u$, saved the residuals $\hat{u_i}$ and draw a histogram of distribution of residuals. If we get something which looks like a familiar distribution, can we assume that our error term has this distribution? Say, if we found out that residuals resemble normal ... |
Just a simple question on parameter selection for SVMs. If I use a minimum finding algorithm to find the optimal parameters for a set of data, how do I "average" the parameters over a set of cross validation runs to come up with the best parameters for my test set run? When doing X-fold cross validation, I can see simp... |
(ignore the R code if needed, as my main question is language-independent)
If I want to look at the variability of a simple statistic (ex: mean), I know I can do it via theory like:
x = rnorm(50)
# Estimate standard error from theory
summary(lm(x~1))
# same as...
sd(x) / sqrt(length(x))
or with the bootstrap like:
li... |
I'm trying to solve this problem out loud. I'm not great at statistics but I try sometimes to understand where things are and where they're going.
I'm trying to figure out the best method to compare the two following datasets. The first is the actual observation and the second is of what my sensor determined the value ... |
I am trying to compare readings from an old flowmeter and a new digital flowmeter to find out if they are significantly different. With the new meter I did readings under different conditions eg. calibrated at 30 seconds, calibrated at 60 seconds, uncalibrated at 30 seconds and uncalibrated at 60 seconds. I want to com... |
Assume we have $n$ points in a rectangular with bound $[0,a] \times [0,b]$, and these points are uniformly distributed in this plane. (I am not quite familiar with statistics, so I don't know the difference between uniformly pick a node in the area $[0,a] \times [0,b]$, or uniformly pick $x$-axis from $[0,a]$ and $y$-a... |
I have one dependent variable and am trying to see if any of my 12 independent variables correlate with it, however I need to check everything for normality.
I understand I need to look at skewness and kurtosis. Does the formula look like the following:
skewness score subtracted by mean of condition divided by the stan... |
I'm having difficulty getting the gradient of the log-likelihood of a multivariate Poisson distribution. Here's how I have it setup:
A finite set of finite-dimensional vectors $T$ with elements $\mathbf{t}$
$d$ functions $\left\{f_1,f_2,\dotsc,f_d\right\}$ with compact support.
The parameter of the multivariate Poisso... |
There is currently a debate whether the EQ-5D score that has a ceiling problem and a bimodal distribution can be used in a linear regression model or not.
Background
The score is very simple and frequently used to assess patient's health related quality of life and consists out of five questions where each has 3 possib... |
I am reading "Monte Carlo Statistical Methods" by Robert and Cassella, and problem 1.3 asks
In example 1.1, the distribution of the random variable $Z=\min(X,Y)$ was of interest. Derive the distribution of $Z$ in the following case of informative censoring, where $Y\sim N(\theta,\sigma^2)$ and $X\sim N(\theta,\theta... |
I've got two sets of measurements whose uncertainty has been quantified: $x_1..x_n$ and $y_1..y_n$. For each $x_i$ and $y_i$, I have not only a point estimate but also an interval that expresses my uncertainty in the accuracy of the measurement.
Now, I'd like to compute a correlation between $x$ and $y$. Is there a rea... |
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