Comparing Variable Selection Techniques on Simulated Data
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Project Overview
(R and Markdown-based Programming Project)
This project simulates multivariate data from three underlying linear models with different amounts of correlation among the predictors, then checks how well several variable selection methods do on each dataset when a linear regression is fit with the variables they pick. We simulate 50 datasets for each of the underlying models in section 7 of Tibshirani’s (1996) paper "Regression Shrinkage and Selection via the Lasso", fit models using several variable selection and shrinkage methods, and compare them on the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), and adjusted R-squared.