Skip to main content

Unanswered Questions

1,559 questions with no upvoted or accepted answers
10 votes
0 answers
2k views

"weight" input in glm.nb function in R. How exactly does the weight affect the likelihood?

I would like to understand how the weight argument of glm.nb is affecting the likelihood function. I understand that glm.nb find ...
9 votes
0 answers
109 views

Name for Generalized Generalized Linear Models

Consider the class of models given by $y\sim F(g^{-1}(\beta^\top\mathbf{x}))$ with $\mathbb{E}[Y]=g^{-1}(\beta^\top\mathbf{x})$. Most authors I've come across call this a GLM only if F is in the ...
9 votes
0 answers
527 views

How do sufficiency statistics help in the interpretation of regression results?

One of the results why canonical link functions are widely used in GLMs is the existence of sufficiency statistics for the regression parameters, which in turn allow for: ... minimal sufficient ...
9 votes
0 answers
6k views

Getting the bootstrap-validated AUC in R

In a paper by Faraklas et al, the researchers create a Necrotizing Soft-Tissue Infection Mortality Risk Calculator. They use logistic regression to create a model with mortality from necrotizing soft-...
8 votes
0 answers
1k views

Alternatives to Cohen's d for non-Gaussian models

Cohen's d (or Hedges' g) are often used to compute effect size. They rely on the assumption of homogeneity of variance across samples however. Because of the pooling of variance that they do, I'm also ...
7 votes
0 answers
345 views

Nonnegative identity-link Poisson regression with ridge or fused ridge penalty

I would like to fit nonnegative identity-link Poisson regression models with a ridge or fused ridge penalty, i.e. with nonnegativity constraints on the fitted coefficients, Poisson error noise & a ...
7 votes
0 answers
460 views

Robust Gamma Regression

I am modeling some spectroscopic data where the response of the instrument to the size of the input is strictly positive and non-linear. Gamma regression seems like a good choice to explain the data, ...
7 votes
0 answers
916 views

Time series models (e.g. ARMA) a type or extension of GLM? Particular/stipulated forms of dependence in time series models

I am trying to understand the relationship between ARMA Time Series models and the GLM (Generalized Linear Model) family of models. As far I know, all GLMs have the following 3 components: 1) random ...
7 votes
1 answer
779 views

Adding a magnitude penalty to a GAM

This is a follow-up to a previous question of mine, explaining the problem in more detail in the hopes of getting more precise advice. Consider the following structured additive regression model or ...
7 votes
0 answers
2k views

How to compare models with different distributional assumptions for response variable in GLM?

Let's say I have measurements $Y$ which are all positive, and the distribution seems to be somewhat skewed. I'm modelling $Y$ in GLM framework. Now I could set my GLM using different distributional ...
7 votes
1 answer
2k views

Geometric Interpretation of Softmax Regression

I'm writing a series of blog posts on the basics of machine learning, just for fun, mostly to validate my understanding of Andrew Ng's class. As I'm currently studying generalized linear models (GLMs),...
7 votes
0 answers
126 views

Zero values and discontinuity in explanatory variable

One of my independent variables measures worker productivity through the variable $\frac{\log{sales}}{\text{# of workers}}$, and I'm creating one variable for skilled and another for unskilled workers....
7 votes
0 answers
2k views

Analysis of survival data using binomial GLM with offset

We are interested in determining whether there's an association between frequency of screening visits and cancer outcomes and whether that differs by race. We have Medicare data to analyze this. ...
6 votes
0 answers
150 views

using `lmer` to fit the linear mixed effects models

Edit: I know some people vote this question is off-topic since it is more like a Cross Validated question. However, I am not here to ask about the coding thing (but I might word in the wrong way). I ...
6 votes
0 answers
1k views

Standard error of the coefficient in GLM

I'm trying to learn about Wald test. I know, that its test statistics is $$ t = \frac{\beta_i}{se\left( \beta_i \right)} $$ But, how is standard error $se$ computed in GLM? I've found only the ...

15 30 50 per page
1
2 3 4 5
104