All Questions
211,620
questions
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How to interpret Location parameters in Generalised Partial Credit Model?
I am struggling to interpret the location parameters in generalised partial credit models.
Say you have location parameters $a_1$,$a_2$ and $a_3$. My professor said that for the item to be accepted ...
- 1,222
1
vote
1
answer
278
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Current chi-square test of homogeneity POST HOC analyses in R
I am looking for a currently-working method of running a chi-square post hoc test.
I've run a chi-square test of homogeneity using chisq.test from stats, however, ...
CommunityBot
- 1
1
vote
2
answers
31
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If a study finds a statistically significant difference, but later is determined to be under-powered, does it matter?
Thanks for any answers in advance.
This question presumes a study has detected a difference between 2 groups, and has determined this difference to be statistically significant.
I'm reviewing a paper ...
- 33.3k
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0
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13
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What would be the effect of assuming the wrong homoskedastic/ heteroskedastic spread of data?
If a cluster trial had homoskedastic data, but the regression model used by the authors used 'robust standard errors' intended for use with heteroskedastic data (or vice-versa), would the implications ...
- 173
8
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2
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559
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Why use the EM Algorithm and not just marginalise the complete likelihood?
On the wikipedia article for Expectation-Maximization it states
Given the statistical model which generates a set $\mathbf{X}$ of observed data, a set of unobserved latent data or missing values $\...
- 148
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26
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Which hypothesis test should I Use? Help pls
I'm doing a personal project and would like to see if High Caloric Food has a statistical significance on Weight Level. I've tried Chi Squared Contingency test as suggested by chatgpt but it gives a p-...
0
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1
answer
1k
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GLM with Gamma distribution: Choosing between two link functions
I need to perform a GLM based analysis on a purely positive, continuous, and highliy right skewed (inflated around low values) outcome variable. I tested several combinations of distributions and link ...
CommunityBot
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44
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Regression analysis with single outcome variable and binary independent variables
This is a meta-analysis. I'm looking at factors associated with vaccine acceptance in many countries. Looking at previous studies in different countries, I recorded factors that are associated with ...
1
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1
answer
11
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Does the use of "robust standard errors" in cluster randomized trials suggest heterskadistic data, implying there is high between-cluster variability?
Please bear with me. I am only recently familiar with some of these concepts. Please correct any poor assumptions.
I am analysing a cluster randomized trial with crossover between intervention and ...
- 109k
1
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1
answer
247
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Expressing one-sided p values of directional hypothesis tests as Bayes factors
Assume we want to test the directional hypothesis that $µ<0$. From a frequentist angle we use a one-tailed $t$-test and imagine we obtain a 1-sided $p$ value of say 0.07, which then would imply ...
- 71.5k
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1
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82
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Using 95% confidence intervals for pairwise comparisons in mixed effects model
I'm running mixed effects models in R using Treatment and Species as crossed predictors and Group as a random effect using the nlme package, like so:
...
- 86.4k
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0
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7
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How to adjust time difference for variables collected at stages of a process?
I'm struggling with my data science project, hoping to get some help in this forum.
I have a data collected from a wastewater treatment facility, with 4 variables, but the variables come from ...
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3
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2
answers
1k
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Calculation of studentized deleted residuals
I have a problem that asks me to calculate studentized deleted residuals from the following set of residuals:
From the original dataset:
The Hat matrix, diagonal elements hii, SSE/MSE, formula for ...
CommunityBot
- 1
1
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1
answer
10
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How can I quantify uncertainty for a least squares estimator in a multivariate linear regression with covariance structure?
Suppose that we have
$$\mathbf{y}\sim\text{N}(\mathbf{X}\boldsymbol{\beta},\sigma^2\mathbf{M}\mathbf{M}'),$$ and let $\boldsymbol{\hat{\beta}}$ be the least squares estimator for $\boldsymbol{\beta}$. ...
- 1,829
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2
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275
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Spearman vs. Pearson for an evenly distributed variable. Can I just choose the coefficent with the stronger correlation?
I am doing my thesis using a a non experimental descriptive correlation analysis with continuous ratio data. One of the variables is unevenly distributed.
I have calculated Pearson's correlation ...
- 408
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1
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79
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Likelihood ratio tests in Growth Mixture Modelling with large sample sizes
I recently conducted Growth Mixture Modelling in a Structural Equation Modelling framework using a sample of n~300,000. I found that for any number of trajectories I fitted, the Vuong-Lo-Mendell-Rubin ...
- 1,222
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61
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As Brier Score = MSE, does MSE in a regression have a calibration-discrimination decomposition?
When the outcome of a supervised learning problem is binary and probabilities are predicted, Brier score can be decomposed into a measure of calibration and a measure of discrimination.
...
- 57.8k
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5
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Is there a way to keep hierarchical clustering order constant across groups for correlation matrices? [closed]
I have created multiple correlational matrices for age ranges (e.g. 1-5, 5-10, 10-15 years old). However, when I do hierarchical clustering using ggcorrplot for example, ggcorrplot(corr_fa_1_mat, hc....
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1
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44
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how do i transform irt discrimination parameters of a 2PL model into factor loadings
I estimated a 2PL model for ordinal data and got discrimination as well as difficulty parameters for each item. To be able to estimate a cut-off value for the discrimination parameter, i wanted to ...
- 1,222
11
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5
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960
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Creating random points in the surface of a n-dimensional sphere
I have a point X in the surface of an n-dimensional sphere with center 0.
I want to create random points following a distribution with center X, the points must be in the surface of the n-dimensional ...
- 99
5
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1
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35
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What is the specific name of this distribution?
I just can’t seem to find the name of this distribution. From my understanding, it is generally applied to pandemics/epidemics. None of the statistics books that I have looked at did not contain any ...
- 57.8k
4
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2
answers
85
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Generate multivariate distributions of lognormal and normal distribution in python
I need to generate random numbers from 3 correlated distributions. First two of them are lognormal and the final one is normal, i.e. for X, ...
- 1,741
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310
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Draw survival curves of 2 groups after multiple imputation on covariates
I wonder how to draw survival curves (Kaplan-Meier) when there is no missing information on the survival variables but on the stratification covariate.
For example, we know for all patients the follow-...
- 342
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1
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41
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Why does my PR Curve look like this?
These are my recall and precision stats for the model I built. The Curve does not look good where recall is 0. Not sure why there are so many points there. Can anyone help and explain why the curve ...
- 3,604
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7
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Error Analysis on two different sized data sets
Here the situation. I have a model plane that records velocity in the x, y, and z axis at 16hz. I have a small data recorder attached to the underbelly of the plane that is also recording the same ...
1
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1
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56
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Good resources for visualizing time-to-event(s) data where event is a continuous variable
I am analyzing patients' rehabilitation use over a 5-year period. Describing their rehabilitation is challenging since:
I should describe the proportion excluded from rehabilitation (zero-inflatation)...
- 342
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0
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10
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Which book help us to do the best EDA for data science?
I am new at data science. I would like to make ML models in orde to make predictions, but I am lost when I have to make interpretations to data... I would really like to understand the data with ...
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0
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13
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What are some applications of copula and how choose which copula to use? [closed]
As shown in the question title, I'm still confused about how to understand and use different copula functions for applications. Could anyone give some examples or intuition?
- 313
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2
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1k
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0
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1
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99
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violation of cox proportional hazard assumption
I'm using cox regression to analyse my data. The explanatory variable is a congenital disease (X) and the outcome is an another disease (Y), which is a comorbidity of the congenital disease. I'm ...
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2
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278
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Not clear why adding additional features (not just transformations) reduces model bias in statistical machine learning [closed]
Setup
In a regression setting, consider the issue of model bias (rather than sampling bias or other statistical biases). If the true data generating process looks like $Y = f(X) + \mathrm{noise}$ and ...
- 3,402
1
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1
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62
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Probability distribution vs probability space
I was trying to understand the difference between the concepts of probability distribution and probability space for what concerns the assignment of probability over the sample space. Wikipedia gives ...
- 16.5k
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0
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9
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Coefficient covariance matrix of inverse probability weighted regression
I am interested in computing an estimate $\hat\Sigma_\hat\beta$ of the asymptotic covariance matrix of the parameter estimates $\hat\beta$ in a regression of $Y$ on $\{X, Z\}$, weighted by weighs $\...
- 31.4k
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0
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12
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Distribution of a concave combination of n poisson variables?
Does a concave combination of n Poisson distributed variables have a closed-form distribution? How would one model it within a GLM framework? As being Tweedie distributed perhaps (I would think that a ...
- 3,040
1
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1
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532
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REINFORCE algorithm, help for the proof of the variance reduction by subtracting a baseline
I'm trying to find a proof or an approximate argument justifying that, in the REINFORCE algorithm, subtracting a baseline to the episode reward reduces the variance. I believe this proof can be done ...
CommunityBot
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4
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1
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85
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Is it appropriate to present predicted probabilities from emmeans for a mixed-effects binomial logistic regression?
I am trying to understand how to analyze data for a generalized mixed model (GLMM) with a binary response. I am interested in visualizing the predicted probabilities, as well as a measure of effect ...
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31
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Measure Theoretic Explanation of Conditional Probability Given a Random Variable and Event
What does it mean in a rigorous measure theoretic sense to have the conditional probability of an event given a continuous random variable (or vector) and an event? As in, suppose $A,B$ are events and ...
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33
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0
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0
answers
26
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Weighted Binary Variable? [closed]
I have a dataset composed of a binary variable, indicating whether agriculture land was state-owned (1) or not (0), repeated for 10 time points. It also includes an interval variable that indicates ...
- 74.5k
1
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1
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21
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Retrospective survival analysis censoring
I'm doing a retrospective survival analysis where I evaluate the recurrence of disease after surgery. The problem is that some of the patients don't come again to the clinic after surgery to do the ...
- 60.4k
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0
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20
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Spatial autocorrelation of car crashes and traffic safety complaints in python
I am admittedly new to spatial autocorrelation and I did this research myself so apologies if this is completely noob behavior. I am trying to determine if there is a correlation between the location ...
- 316k
2
votes
1
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244
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How to analyse binary responses for various factors, including interactions: chi square, mixed models, logistic regression, or ANOVA on percentages?
I run an experiment where subject had to recognize an emotion from various musical stimuli (which were composed with a certain emotional intent). There were 4 levels of emotional_intent, subjects ...
- 74.5k
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0
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7
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Covariance matrix of a dataset with high variance tends to the covariance matrix assuming it were mean-centered [closed]
Basically that for a dataset $D = \{x_1, x_2 \dots x_n\}$ and total variance of ${TV}_D \gg 0$, or the limiting case perhaps ${TV}_D \to \infty$:
$C = \frac{1}{n}\Sigma_{i=1}^n(x_i - \bar{x})(x_i - \...
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69
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Can the calibration-discrimination decomposition of Brier score be viewed as the bias-variance decomposition of mean squared error?
The mean squared error has a famous decomposition into bias and variance.
$$
\text{MSE} = \text{bias}^2 + \text{var}
$$
Brier score is also a mean squared error calculation, and Brier score has a ...
- 57.8k
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0
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11
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Homogeneity of regression slopes and collinearly of covariates in ANCOVA [closed]
Thank you for such a great website for statistics learners, I am currently studying a factorial ANCOVA and have two questions:
1)do we need to test homogeneity of regression slopes for an interaction ...
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5
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0
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94
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How can statistics be used to avoid "Lending False Credibility To Decisions We've Already Made" [closed]
In light of this article Data Science Has Become About Lending False Credibility To Decisions We've Already Made published in Forbes, I would appreciate input from the statistical and data science ...
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16
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Analogue of landscape conjecture in likelihood theory or Bayes?
The so-called landscape conjecture in machine learning says that in high dimensions, most critical points of the loss surface are saddle points rather than poor local minima.
Out of curiosity I was ...
- 939
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10
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Convexitiy of multi-class hinge loss
The empirical risk of a multi-class hinge-loss is given by
$$L(\Theta,(x,y) = \max_{j \neq y} \Big[1+ \sum_{i=1}^{d} x_i(\Theta_{ij} - \Theta_{iy}) \Big]_{+} $$
where $x \in \mathbb{R}^{d}$ is a ...
- 195
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0
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7
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using standardized residuals from a previous chi-square analysis as an IV in a regression
I have an analysis I'm considering, and I'm not familiar enough with the statistical details to tell if there are any potential errors. I've done my best to describe the analysis briefly below.
I ...
2
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0
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37
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How can we justfify the assumption of equal scale/variance in the definition of R-squared from Deviances in GLMs? [closed]
If we take the R-squared to be the comparison of Deviances between models (the model of interest, the saturated model, and the constant model), we can write it as (see this answer CC BY-SA 4.0):
$$R_{...
- 18.3k