High-dimensional model selection with the modelSelection R package

This two-day course offers practical, hands-on training in statistical model selection, comparison, and evaluation using R. Participants will learn to choose among competing models, handle multiple pr...

EDIT: This was an attempt to write guidance. It turns out I stepped quite far from my depth and the text sounded much more conclusive than it should. I think it is correct to currently just classify i...

Easton's Nook is an intimate urban writing and arts retreat space where scholars, artists, and activists can meet, and can pursue their work individually or as a part of small, supportive, collaborati...

Researchers have used contorted, inefficient, and arbitrary analyses to demonstrated added value in biomarkers, genes, and new lab measurements. Traditional statistical measures have always been up to...

AWS outlined a structured, metrics-based approach to choosing the right large language model for specific use cases, promoting its 360-Eval framework to replace informal “vibes-based” testing with dat...

Model selection and averaging for regression, generalized linear models, generalized additive models, graphical models and mixtures, focusing on Bayesian model selection and information criteria (Bayesian information criterion etc.). See Rossell (2025) &lt;<a href="https://doi.org/10.5281%2Fzenodo.17119597" target="_top">doi:10.5281/zenodo.17119597</a>&gt; (see the URL field below for its URL) for a hands-on book describing the methods, examples and suggested citations if you use the package.

This topic is for discussions about Statistically Efficient Ways to Quantify Added Predictive Value of New Measurements

What are some of the problems with stepwise regression?