Regularized Relative Risk Regression

The relative risk (RR) offers interpretation and comparison advantages over the Odds Ratio (OR) used in logistic regression. However, its direct estimation in high-dimensional settings is challenging. Common approaches, such as penalized log-binomial and Poisson regression, are built on parameters that are variationally dependent, while newer, variation-independent models have been limited by estimators not designed for high-dimensional or sparse data.

To address this, this project built on previous penalized RR models to implement a faster penalized estimator for the variation-independent relative risk model. The contributions include an efficient implementation in C++, the use of an Adaptive Step Size FISTA algorithm for robust optimization, and a comprehensive evaluation of different penalization strategies and model specifications. Through simulation studies, the proposed estimator is shown to be a robust tool for high-dimensional analysis. It demonstrates better predictive accuracy and the ability to identify relevant predictors in sparse scenarios correctly.

To join this seminar virtually, please request Zoom connection details from ea@stat.ubc.ca.