Comparing Variable Selection Techniques on Simulated Data

Deployed Programming Project

GitHub Code Repository

Project Overview

(R and Markdown-based Programming Project)

The aims of this project are to simulate multivariate data from three underlying linear models with varying degrees of correlation among predictors and then observing how effective different variable selection methods perform on each dataset by fitting a linear regression model with the selected variables. Thus, we first simulate 50 datasets for every underlying model from section 7 of the Paper "Regression Shrinkage and Selection via the Lasso" by Tibshirani (1996). We then fit the models according several variable selection and shrinkage methods and consider Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Adjusted R-Squared Value as performance metrics.