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We define the $\chi^2$ discrepancy as $$T(y,\theta)=\sum_i\frac{(y_i-E(y_i|\theta))^2}{\mathrm{var}(y_i|\theta)}$$
(definition from Gelman). How do we apply this if $\theta$ is a random variable, and we have samples of it(but not the distribution)?
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One of the late contributions of R.A. Fisher was fiducial intervals and fiducial principled arguments. This approach however is nowhere near as popular as frequentist or Bayesian principled arguments.
What is the fiducial argument and why has is not been accepted?
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The coefficient of variation is defined as the ratio of S.D to mean, a dimensionless number which on multiplication by 100 gives the percentage of dispersion with mean. But when we square this coefficient of variation, what does it signify?
(I have to understand a graph having squared coefficient of variation on y axis... |
Are there any speedy alternatives to the EM algorithm for learning models with latent variables (especially pLSA)? I'm okay with sacrificing precision in favor of speed.
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I'm using euclidean distance for kNN. I have labeled data, I have took logarithm of some variables to make them look more like normaly distributed and scaled them all. And now I would like to multiply some variables by weights, then compute euclidean distance and train kNN. But how to find those weights ? My idea is to... |
I have a question about different ways of specifying a response variable in a model and what effect this could have on my results.
In my example, I wanted to model the number of times a bird visits a nest to feed its young during a set period of time. So the data would be a set of counts, let's call it Number of Visit... |
What kind of plot are normally used in Generalized linear model and what are their interpretations?
Especially for Standardized deviance residual vs fitted value plot, what can we see from the plot?
|
I'm analysing reaction time data using mixed-effect modelling in R. Data comes from 2 types of participant groups: native speakers and non-native speakers. For the non-natives, I have proficiency scores (estimating their mastery of English). The proficiency of native speakers is irrelevant, and coded as NA. Does th... |
According to Bayes theorem $P(A|B) = \frac{P(B|A)*P(A)}{P(B)}$
I've found somewhere that: $P(x_t|z_{1:t}) = \frac{P(z_t|x_t)*P(x_t|z_{1:t-1})}{P(z_t|z_{1:t-1})}$ but I don't really understand it, is it expressed according to the bayes theorem ?
This is from page 13 of http://www.igi.tugraz.at/pfeiffer/documents/particl... |
I have to compute a similarity measure between different sets (Actually they are more like maps than sets). A weight is associated to each element of the set.
The sets I want to compare represent different journal or conferences. Each element of the set is an author and the associated weight is the number of papers pub... |
I have a set of scores on a specific dataset given by human evaluators, let's call it HJ. I also have the scores of two computer models on the same dataset, let's call these sets M1 and M2. I have calculated Spearman's coefficients for (HJ,M1) and (HJ,M2), which show that M2 is closer to human judgements than M1. Now I... |
I am using constrOptim to minimize a log likelihood function for maximum likelihood estimation of parameters.
I wish to set the bounds on my parameters, but to not understand the constrOptim definition of the feasibility region.
The feasible region is defined by ui %*% theta - ci >= 0
I have a set of parameters with bo... |
I have successfully implemented a maximum likelihood estimation of model parameters with bounds by creating a likelihood function that returns NA or Inf values when the function is out of bounds. I optimize the function using optim in R.
detailed example available on github
Quick example:
likelihood.fun<-function(par... |
Given an output from optim with a hessian matrix, how to calculate parameter confidence intervals using the hessian matrix?
fit<-optim(..., hessian=T)
hessian<-fit$hessian
I am mostly interested in the context of maximum likelihood analysis, but am curious to know if the method can be expanded beyond.
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Can I use a T-Statistic to construct a Confidence Interval for a sample collected from a large population and without knowledge about the population's standard deviation? I know the "model" answer is that a sample, even if the sample size $n$ is small, would have to use a Normal Z statistics if it was collected from a ... |
My problem: parallel group randomized trial having a very right-skewed distribution of the primary outcome. I do not want to assume normality and use normal-based 95% CIs (i.e. using 1.96 X SE).
I am comfortable expressing the measure of central tendency as the median, but my question is then how to construct a 95% CI ... |
Using longitudinal survey data on children using psychotropic medications, we are interested in estimating associations with medication classes, their persistence and adherence (longitudinal exposures), and certain rare outcomes such as suicidal idealization. There are clusters defined at the child level with no cluste... |
How to understand the relationships, comparative advantages, and comparative disadvantages of boosting, bootstrapping and bagging in terms of their respective applications in data mining.
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In the Stanford ML course, we were taught to find good values for the lambda parameters of ridge/lasso by iterating for various lambda values on several cross-validation sets and picking the values which correspond to the hypothesis with the minimum CV error.
The problem is, I am playing with a big data set (which migh... |
I am training a large set of neural networks for a quite simple task. 10 of the networks have the same configuration, but have different amount of data. The 10 networks each have one hidden layer, with 2 neurons in it. The first network gets 1.000 training examples, the next 2.000 and the last one gets 10.000 training ... |
My question arises from my current task to develop a clinical prediction model using ordinal logistic regression (with rms), but applies to any kind of regression analysis.
The proportional odds assumption (difficult to assess...) is only roughly met. (I am aware of the continuation ratio approach).
Results of measure... |
I'm an Industrial Engineering Major and I'm looking out for book(s) on Probability and Statistics (Combined would be preferred but separate would do as well) which :
I, as a self learner, can work with.
Is concise but covers : Statistical methods for data analysis. Descriptive plots and statistics; observational studi... |
As I understand, most of the research is centralized through the U.S. Census Bureau. However, I couldn't find any overview of fields of research on their website. Is there any other institution or website that summarizes the research that has been done?
I'd especially like to know which data is of most interest to rese... |
I'm doing a linear regression with a transformed dependent variable. The following transformation was done so that the assumption of normality of residuals would hold. The untransformed dependant variable was negatively skewed, and the following transform made it close to normal:
$$Y=\sqrt{50-Y_{orig}}$$
where $Y_{ori... |
For example, in linear regression, we assume the errors are normally distributed, have constant variance and have an expected value of 0. These assumptions all refer to the population. But we look at our actual data points to see if these assumptions are satisfied, Why do we do this? How do we know that results hold fo... |
I am working on comparing a number of investments and am attempting to do a relative risk comparison but am having troubles ensuring accuracy with the standard deviations. A number of the observations have a shorter time horizon than I would like to compare (some have only a 3,4, or 5 year time horizon). I would like... |
Consider the following PCA biplot:
library(mvtnorm)
set.seed(1)
x <- rmvnorm(2000, rep(0, 6), diag(c(5, rep(1,5))))
x <- scale(x, center=T, scale=F)
pc <- princomp(x)
biplot(pc)
There are a bunch of red arrows plotted, what do they mean? I knew that the first arrow labelled with "Var1" should be pointing the most v... |
I turn to this forum for advice with the following problem. If you could please shed some light on any aspect of this question I'd be very grateful.
Problem decription:
I'm trying to use an SVM to segment a grayscale image of a puncture in polymer (original res. 1280x1024, can't post, no reputation :)
Now, I know this ... |
I am reading about influence diagrams, and I want to know how their utility function is calculated.
In all of the examples listed in the literature, I am not able to find the formula of the utility function.
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am doing my dissertation involving cox model and i would like to understand how you interpret the survival table at mean of covariates. how i do u i determine the survival function from the output
Survival Table
At mean of covariates
Time Baseline SurvivalSE Cum Hazard
0 .023 .991 .006 .009
3 .033 ... |
I'm trying to run a normality test on the residuals after fitting a mixed-effect model (with lmer). I read that the Shapiro-Francia test can deal with data with more than 5000 observations (I have more than 8000), but when I run it I get an error:
sf.test(resid(dat.lmer11))
Error in sf.test(resid(dat.lmer11)) :
sa... |
I am struggling to find a solution on finding the distribution of following random variable:
$$Y = Z \cdot |X|$$
here, $Z$ is a random variable takes 1 or -1 with equal probability, and $X$ is a standard normal variate, and $|\cdot|$ denotes absolute value.
Can somebody help me with some pointer from where I should st... |
I have a simple question: does any data gathered over time could be treated as a time series? For instance, if I measure CPU load at certain intervals, would that be a time series? Actually, I'm not interested on actual time of the measurement. I could simply mark the points along x-axis as 1, 2, 3, ... -- what is rele... |
Those lines are based on a lowess fit of points I have.
I would like to be able to extrapolate those lines up to x=500 for instance. I would like to extend this plot and see if those lines reach a plateau or not.
Is there a way to do this ?
The CSV data for the blue line is here: http://db.tt/GHhAGLtM
The idea is fi... |
I am trying to do Gibbs sampling, from this paper.
This is a CIR financial model, I want to do Gibbs on its parameters:
$$y(t+{\Delta}^{+})=y(t)+(\alpha-\beta y(t)){\Delta}^{+}+\sigma \sqrt{y(t)}{\epsilon}_{t}$$
The conditionals are:
Step 1: ${y}_{t,j+1}^{*}|{y}_{t,j}^{*},\theta\ \tilde{\ }\ N({y}_{t,j}^{*}+(\alpha-\... |
I am comparing images for correlation. The images are all correlated, but I would like to determine when one pair is much more highly correlated, relative to another pair. I am using as a statistic the difference in the logs of p-values produced by a Spearman rank correlation test. If this is difference is small, there... |
Why is it dangerous to initialize weights with zeros? Is there any simple example that demonstrates it?
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What is the exact definition of precision of a statistical estimator? Any references to textbooks are appreciated.
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I have been evaluation various regression techniques over a regression dataset . I am surprised by the fact that cross-validated RMSE of Lasso is better than SVM and Random Forest in my case.
Can this happen? I believed that a non-linear modelling technique like random forest or SVM would do better than a linear model ... |
I am analyzing an experiment that has the following design:
Factor1 Factor2
Level1 Level1
Level2
Level2 Level1.1
Level1.2
Level2
In other words, Factor2 has different number of levels across Factor1. I'd like to use logistic regression, but how should I set it u... |
for example, in a discrete distribution A
P(X) = 0.999 when x = 0
P(X) = 0.001 when x = 1000
E(X) = 1
Var(X) = 1000-1=999
compared to B
P(X) = 0.6 when x = 0
P(X) = 0.2 when x = 1
P(X) = 0.2/2 when x = 2
P(X) = 0.2/4 when x = 4
P(X) = 0.2/4 when x = 8
E(X) = 1 =A
Var(X) = 0.2+0.4+0.8+3.2-1=3.6 <<A
However B ... |
I have a bunch of related datasets. The pearson correlations between pairs of them are typically definitely larger than the spearman correlations. That suggests any correlation is linear, but one might expect that even if the pearson and spearman were the same. What does it mean when there is a definite gap between the... |
I have social network data in which an "ego" names a friend "alter". I am running a regression in R in which attributes of alter are predictors of outcomes for ego. So each observation is dyadic with variable measures for both ego and alter.
There are multiple observations for each ego which are accounted for by using... |
This footnote by Efron appears in the chapter on false discovery rate control:
I am trying to avoid the term "significant" for the rejected cases as
dubious terminology even in single-case testing, and worse in the
false discovery rate context, preferring instead "interesting".
--Bradley Efron "Large Scale Infe... |
I am interested in determining if the decisionmaking of a particular government body was responsive to policy and statutory changes that occurred at known points in time.
I can classify the decisions of this body as 1) rejected proposal; 2) accepted modified proposal; 3) accepted proposal (without change).
I could clas... |
I am simulating a game of Roulette (in R), where I simulate a gambler that starts with £100, spins the wheel 10 times according to some strategy (e.g. bet £5 each spin on red). At the end of this simulation the bank balance will be either higher or lower than £100. If higher, the spin is considered a win, otherwise a... |
I am just starting out self-learning in time series analysis. I have noticed that there are a number of potential pitfalls that are not applicable to general statistics. So, building on What are common statistical sins?, I would like to ask:
What are common pitfalls or statistical sins in time series analysis?
This is ... |
I have a prediction outcome that is dichotomous while all other predictor variables are binary and categorical.
How can I perform cross validation in SAS and see the ROC curve?
I need to create bayesian network.
|
I've always thought the emergence of the normal distribution was kind of magic, specifically that $\pi$ and $e$ both emerge in the distribution, even though they don't exist in the binomial.
I've googled heavily, but haven't found a very good guide showing how these constants arise, but would like to see it.
I'm horrib... |
This sounds like a problem that has to be well known, but I can't find a good answer. I have tried another forum before I realized there was a stackexchange stats site. So here goes ...
I have two competing models for the fraction of special objects $n$ in a sample $N$. One model predicts that $f= \frac{n}{N} = 0.15$ o... |
I'm trying to fit a model under JAGS where I have a response, y, that depends linearly on eight predictors (with no intersect : I'm actually trying to evaluate the share of each socioeconomic statuts population that voted for a candidate, wth aggregated data).
My problem is that naive hierarchical modelling, under lme... |
I have two classes and, in total, 40 subjects (20,20). I used an SVM to implement classification and did a leave-one-out cross validation to get an overall performance of the classifier.
How could I draw an ROC curve for this situation?
Is it even necessary to draw a ROC curve in this situation?
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In the attached images (bottom of this post), I wish to compare the plotted distributions as follows. Each plot shows two distributions labelled 'Forwards' and 'Backwards'. For each attached example, I wish to compare each of 'Forwards' and 'Backwards' of sub-figure a with 'Forwards' and 'Backwards' of sub-figure c (an... |
I am training a neural network with time dependent financial data. In order to avoid overfitting I would like to stop the training at the point where my neural network stops improving on a set of validation data, different from the training data.
My question is how to best divide my data in training and validation dat... |
I guess my question comes in two parts:
Following a 1x7 repeated-measures ANOVA, I carried out 3 pre-planned tests. Two were t-tests between 2 of the conditions. The third is a 1x6 ANOVA (to test significance between conditions 2-7). My question is:
Do I have to apply the Bonferroni correction to all 3 tests, or j... |
I posted this on stackoverflow.com yesterday and its had very few views. I came across this stackexchange site and thought its got to be worth an ask:
I have an interesting problem and I'm sure there is an elegant algorithm with which to solve the solution but I'm having trouble describing is succinctly which would he... |
I am looking for a good tool in R or Python or any other implementation that can help to me generate sampling from hierarchical Pitman-Yor processes (HPY) (one of the recent and popular nonparametric Bayesian methods) to do language modeling.
Any help or pointer is appreciated.
|
I have a data set with sales as $Y$ and total retail shelf space ($x$).
I want to investigate the diminishing return, i.e. whether additional shelf space would contribute to more sales. I did regression or Arima model where I found that the squared shelf space ($x^2$) was significant. Thus there is a diminishing retur... |
Say, $\psi | Y, {Y}^{*}, {\sigma}^{2}$~$N(\mu, {\Lambda}^{-1})$ this is a bivariate normal
Does the ${\Lambda}^{-1}$ actually mean precision or variance. I was told in some Bayesian papers, the second parameter actually means precision. I didn't think so, but maybe someone else has read more paper would know?
The ... |
I recall having been told that to generated $p$ variate Marshall Olkin distribution, one proceeds as follow:
For $p=2$, generate $X_1,X_2,X_3\sim exp(\lambda)$, then $(Y_1,Y_2)\sim MMO$
where $Y_1=\max(X_1,X_2)$ and $Y_2=\max(X_1,X_3)$.
But I must have gotten the definition wrong because this doesn't look like
a 'MM... |
I have an index variable that is calculated as the sum of $N$ random variables:
$Y = \sum_{i=1}^N x_i$.
I would like to come up with a subset, $\mathcal{S}$, of the $x_i$ such that
$Z \equiv \sum_{i\in\mathcal{S}} x_i$
is as highly correlated with $Y$ as possible. More specifically, I would like to be able to either ... |
Suppose that one can construct an unbiased estimator $X$ of the quantity $E$, is there a way of getting an unbiased and positive estimator of $E^2$? Indeed, if $X_1$ and $X_2$ are two independent copies of the estimator $X$, the quantity $X_1 X_2$ is an unbiased estimator of $E^2$, but is not positive in general.
Motiv... |
I intend to give a one hour lecture to teenagers about statistics. I will probably see them only once. This scenario might happen over and over.
I would like to give them some activity to make them experience statistics. But I am forced to do it with people who do not know anything about probability, statistical inf... |
I would like to test several (multivariate --between 5 and 20 dimensions) depth measures empirically. I'm looking for a couple of test-benches (read distributions). These should be:
non-elliptical,
absolutely continuous,
uni-modal
have convex contours (if you prefer, level sets)
have non independent components.
Eas... |
I am doing my thesis and have absolutely no previous experience in statistics.
I have constructed several Likert scales by forming composites scores each based on 4-6 items which tests the level of agreement of my respondents.
Specifically, I have created two scales 'purchase behaviour' and 'website appeal'. I want ... |
This is a question that's been bugging me for some time. The problem is this: I'm modelling the residuals of a model $f(t,\vec{\theta})$ with (what I think is) an AR process plus a white noise process via MCMC using a multivariate gaussian likelihood, where I model the covariance matrix elements equal to the elements o... |
In the middle of an argument that I will present to my students I'll have to prove that $E[X^2]\geq E^2[X]$, but I don't want to use Jensen's inequality to do so. Is there any elementary way to go?
Thanks!
|
One standard model used with panel data is fixed effects: $y_{it} = \mu_i + \theta_t + \epsilon_{it}$, where $i$ is the individual and $t$ is time subscripts. This can be estimated easily with OLS and dummy variables.
The model assumes that there is a single underlying time series, $\theta_t$ for $t = 1,2,...,T$. All i... |
All of my statistical training has been about dealing with samples (psych background). I am involved in a project where we have a census dataset (demographic data from all the relocation sites throughout Phnom Penh, Cambodia). I am not certain if the usual methods of comparing group means, such as using an ANOVA or Kru... |
I have a minimisation problem in which the parameters are a mix of integers and scalars. Some of the integers have a small range, around 0-10 but others range in the thousands. To give some context, these are all hyper-parameters of machine learning algorithms. At each iteration for a given set of parameters a series o... |
A number of well known moving averages, such as Spencer's 15 point MA, and Henderson moving averages have negative weights in the averages.
What does this mean in a conceptual sense? What information are you trying to obtain from the data corresponding to those terms? Why is this more sensible than just weighting those... |
I need to determine at what value of a variable mictrb there would be almost no correlation between two other variables: Equivalent width and Abundance (description and graphs below).
I have been told that when mictrb = 0.9, then there is no correlation between Equivalent Width and Abundance. The first chart below, I'v... |
I have calculated the bivariate linear correlation coefficient between $x$ and $y$ and it is $r= 0.45$; $p<001$.
I know $r$ is a measure of effect size.
Should I also be reporting the confidence interval in this case?
If yes, how can it be justified? i.e. why is it important to report it together with the effect size... |
I am not very comfortable with the (parametric) assumptions made in cointegration analysis. I have recently found that there is a way to carry out nonparametric conintegration analysis (a google search with the term 'nonparametric cointegration' yields several results).
Is anyone aware of an R package that allows for n... |
I am working on time series prediction. I have two data sets $D1=\{x_1, x_2,....x_n\}$ and $D2=\{x_n+1, x_n+2, x_n+3,...., x_n+k\}$. I have three prediction models: $M1, M2, M3$. All of those model are trained using samples in data set $D1$, and their performance is measured using the samples in data set $D2$. Let say ... |
It is widely known that one cannot validly test a hypothesis using the data in which the hypothesis-generating feature was observed. So one needs at least two samples, one for generating hypotheses and one to test them.
But the two samples need to be similar in some way, eg. the finding that shoe size correlates with i... |
I'm trying to understand the concept of the expected value.
Especially, what bothers me is the expected value for discrete random variables. I will try to formulate it by examples:
The expected value when throwing a six-sided die is $E(X) = 3.5$, which as I see it, is not even part of the probability space $\Omega = \... |
Suppose I have $N$ independent random numbers with a uniform distribution in $[+1, -1]$. Suppose I take the discrete Fourier transform of these numbers. What would the probability distribution of each frequency coefficient be?
Each frequency coefficient is the weighted sum of $N$ independent random numbers with the sam... |
When doing research in Economy, one frequently needs to verify theoretical conclusions on real data. What are reliable data sources to use and cite? I am mainly interested in sources that provide various statistical data such as GDP, population, CPI, PPI etc.
EDIT:
Here's an aggregation of the links appearing in this t... |
data a;
input treat$ rep boreria mimiosa asysta axono seleria paspa macran melas scopo cleom otto;
cards;
M 1 0.20 0.00 0.00 0.00 0.00 12.20 0.00 0.00 0.00 4.90 0.00
M 2 0.00 0.00 0.00 0.64 0.00 0.00 0.00 0.00 0.00 0.00 0.00
M 3 0.00 ... |
I have 2x2 design with n = 3 (averages) for each group (see here). I am not sure that my data follows any particular distribution, but I would like to use confidence intervals as error bars in a dot plot. Thus I can't really use sd, se or bootstrapping from a distribution. The approach I am after is described in this a... |
We recently released a new redesigned e-commerce website for a client. They are claiming to be seeing a 40% drop in revenue due to the changes. We have daily sales data going back a few years. We are trying to determine if a recent drop in revenue after a site relaunch can be attributed to the redesign or just due to... |
Suppose I have some data sitting in front of me, which is supposed to be sampled from a specific distribution. How would I go about testing whether it is from that distribution?
I've seen several tests which appear to do this - Pearson's chi-squared test, the one-sample Kolmogorov-Smirnov test, and the Anderson-Darling... |
I have a questionnaire with a part (a) and part (b). It looks like this:
(a) "Do you ever feel sad":
0="never", 1="sometime", 2="often", 3="nearly always"
(b) "How distressed are you by this":
0="not distressed", 1="a bit distressed", 2="quite distressed",
3="very distressed".
I want to create a new va... |
When I analyze my variables in two separate (univariate) logistic regression models, I get the following:
Predictor 1: B= 1.049, SE=.352, Exp(B)=2.85, 95% CI=(1.43, 5.69), p=.003
Constant: B=-0.434, SE=.217, Exp(B)=0.65, p=.046
Predictor 2: B= 1.379, SE=.386,... |
What's the easiest way to run permutation tests for a bivariate Pearson correlation matrix in R?
I have tried running MPT.Corr, Multivariate Permutation Test
for Correlations by Urbano Blackford et al., but I am not sure this function is applicable to the problem.
The data contains mainly Likert scale items and time me... |
I was hoping that I could get some feedback in terms of keywords I should searching for, or concepts I need to consider with the following:
I have non-experimental data that I would like to model in a regression format. Unfortunately, I only have 12 subjects and a set of repeated measures of 6 observations: spring, su... |
Is there a way to simplify this equation?
$$\dbinom{8}{1} + \dbinom{8}{2} + \dbinom{8}{3} + \dbinom{8}{4} + \dbinom{8}{5} + \dbinom{8}{6} + \dbinom{8}{7} + \dbinom{8}{8}$$
Or more generally,
$$\sum_{k=1}^{n}\dbinom{n}{k}$$
|
I have a factor analysis model defined by:
$x = m + Wz + e$
where $x$ is a p-dimensional visible variable, $m$ is a constant vector, and $z$ is a $n$-dimensional Gaussian latent variable with $z$ ~ $N(0, I)$, $W$ is a $p\times m$ matrix and $e$ is a $p$-dimensional with $e$ ~ $N(0, \Psi$) - it is the factor loadings ma... |
I am trying to analyze repeated measures data and am struggling to make it work in R. My data is essentially the following, I have two treatment groups. Every subject in each group is tested everyday and given a score (the percentage correct on a test). The data is in the long format:
Time Percent Subject Group
1 ... |
I was reading a book and the authors metioned that risk exposure can be estimated scientifically using this forumula:
$risk(\$) = \frac{(a + 4m + b)}{6}$ and standard deviation $\sigma = \frac{b-a}{6}$
Where:
a = minimum dollar exposure
m = most likely dollar exposure
b = maximum dollar exposure
Where a, m, b are soli... |
I'm reading Andrew Ng's notes on machine learning, and on page 12 of this document, he makes a step in his proof that I'm trying to decipher:
Let $\textbf{x} = \left( 1 , x_1 , x_2 , \cdots , x_n \right)^T$, a vector of variables, and $\theta = \left( \theta_0 , \theta_1 , \theta_2 , \cdots , \theta_n \right)^T$, a vec... |
I'm looking for an efficient algorithm to detect Tomek links. I'm wondering if anyone knows where to find it.
This is the definition of Tomek links:
Suppose $\{ E_1,\ldots,E_n\} \subset R^k$ is a dataset, with each $E_i$ having exactly one of two labels $+$ or $-$. A pair $(E_i,E_j)$ is called a Tomek link if $E_i$ ... |
In a related question, I had asked about the norm induced by an inverse Wishart matrix. I am interested in generalizing that result somewhat. Let $A\sim\mathcal{W}_p\left(I,n\right)$, a Wishart matrix with scale matrix $I$, the identity, and $n$ degrees of freedom. Let $l$ be some fixed $p$-dimensional vector. Consider... |
I have a simple random sample (SRS) $S$ of size $n$ drawn from a population $D_1$ containing $N_1$ members. A new set $D_2$ with $N_2$ members is added to the population. I would like to create a new set $S'$ that is a SRS from $D_1 \cup D_2$.
One strategy that won't work is to draw a SRS of size $nN_2/N_1$ (or any ... |
Binomial logistic regression has upper and lower asymptotes of 1 and 0 respectively. However, accuracy data (just as an example) may have upper and lower asymptotes vastly different to 1 and/or 0. I can see three potential solutions to this:
Don't worry about it if you are getting good fits within the area of interest... |
I need to select a statistical test in order to answer the question of whether the TASR238 genotype is a good predictor of the ability to taste PTC (phenotype)
Here are the data I collected for my lab report:
PTC taster
Genoty Strong Weak Non
T/T 10 4 2
T/t 30 26 12
t/t ... |
I would like to run some two dimensional Kolmogorov–Smironov tests to determine whether a two-dimensional distribution fits with a reference.
Is there any package or application that I could use in a relatively straightforward fashion? Or is there a different algorithm that is preferrable? I have just a basic statistic... |
In the text mining, besides N-gram model, what are the state-of-art models for building feature space while capturing the dependence among the different words, or capturing the semantic meaning in the article?
|
I am trying to build a model to explain an ordinal response variable $y$ with 4 levels: $y_0$, $y_1$, $y_2$ and $y_3$. The independent variable in this model is $v$. $v$ is a categorical variable with three categories $v_a$, $v_b$ and $v_c$. The assumption of proportional odds fails for $v$. Therefore, I am using the ... |
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