Projection predictive variable selection using Stan+R

Abstract

This document is additional material to our previous study comparing several strategies for variable subset selection. Our recommended approach was to fit the full model with all the candidate variables and best possible prior information, and perform the variable selection using the projection predictive framework. Here we give an example of performing such an analysis, using Stan for fitting the model, and R for the variable selection.

References in corpus (1)

• Comparison of Bayesian predictive methods for model selection

Cited by in corpus (6)

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• Comparison of Bayesian predictive methods for model selection
• Pareto Smoothed Importance Sampling
• High-Dimensional Bayesian Regularised Regression with the BayesReg Package
• On the Hyperprior Choice for the Global Shrinkage Parameter in the Horseshoe Prior
• Selection of inverse gamma and half-t priors for hierarchical models: sensitivity and recommendations