Split Regularized Regression

TL;DR

This paper introduces a novel split regularized regression method that partitions covariates into groups with the aim of improving prediction accuracy by encouraging sparsity and diversity, building on existing penalized linear regression techniques.

Contribution

It proposes a new approach for splitting covariates into groups with regularized estimation, enhancing prediction accuracy over traditional methods like Lasso and elastic net.

Findings

Method improves prediction accuracy in simulations

Consistent estimation with increasing predictors

Effective in real-data applications

Abstract

We propose an approach for fitting linear regression models that splits the set of covariates into groups. The optimal split of the variables into groups and the regularized estimation of the regression coefficients are performed by minimizing an objective function that encourages sparsity within each group and diversity among them. The estimated coefficients are then pooled together to form the final fit. Our procedure works on top of a given penalized linear regression estimator (e.g., Lasso, elastic net) by fitting it to possibly overlapping groups of features, encouraging diversity among these groups to reduce the correlation of the corresponding predictions. For the case of two groups, elastic net penalty and orthogonal predictors, we give a closed form solution for the regression coefficients in each group. We establish the consistency of our method with the number of predictors possibly increasing with the sample size. An extensive simulation study and real-data applications show that in general the proposed method improves the prediction accuracy of the base estimator used in the procedure. Possible extensions to GLMs and other models are discussed. The supplemental material for this article, available online, contains the proofs of our theoretical results and the full results of our simulation study.