Using distance covariance for improved variable selection with applications to genetic risk models

TL;DR

This paper introduces a novel variable screening method based on distance covariance and correlation, which is distribution-free and model-free, effectively addressing high-dimensional challenges in genetic risk prediction.

Contribution

It proposes a new feature screening procedure utilizing distance covariance and correlation that requires no distributional assumptions or regression model specification.

Findings

Effective in high-dimensional genetic risk models

Handles uncertainty via cross-validation

Addresses classification challenges with reject options

Abstract

Variable selection is of increasing importance to address the difficulties of high dimensionality in many scientific areas. In this paper, we demonstrate a property for distance covariance, which is incorporated in a novel feature screening procedure together with the use of distance correlation. The approach makes no distributional assumptions for the variables and does not require the specification of a regression model, and hence is especially attractive in variable selection given an enormous number of candidate attributes without much information about the true model with the response. The method is applied to two genetic risk problems, where issues including uncertainty of variable selection via cross validation, subgroup of hard-to-classify cases and the application of a reject option are discussed.