Unanswered Questions
3,344 questions with no upvoted or accepted answers
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"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 ...
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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 ...
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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 ...
- 1,129
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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-...
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Calculate goodness-of-fit (with deviance) to compare averaged models?
I need to compare the goodness of fit of several averaged logistic regression models by calculating the deviance explained. I'm using the MuMIn package in R to ...
CommunityBot
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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 ...
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Overfitting a neural network to a single batch as a sanity check - how small a loss value is small enough and long to run for?
I'm currently developing a neural network for a regression task. Following on from the advice given in places like here, here, and here I'm attempting to overfit my model to a single batch of 5 ...
CommunityBot
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Identifying non-linearities in relationship between variables
Logistic regression is often used to identify the effect of $x$ on a binary variable $y$ after adjusting for potential confounders $x_1,...,x_n$. In the medical literature, I will sometimes encounter ...
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What are the differences between HC estimators and their small sample properties?
I am currently using R to run regression with the following code:
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- 468
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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 ...
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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, ...
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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 ...
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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 ...
CommunityBot
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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 ...
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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),...
CommunityBot
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