A Two-Stage Dimension Reduction Method for Induced Responses and Its Applications

TL;DR

This paper introduces a two-stage dimension reduction method to efficiently estimate the central subspace for induced responses, addressing high-dimensionality issues in biological data analysis, including censored responses and biomarker applications.

Contribution

It proposes a novel two-stage estimation procedure for the central subspace of induced responses, improving efficiency over existing methods, with extensions to censored data and practical applications.

Findings

The method outperforms existing techniques in simulations.

Effective in handling censored responses.

Demonstrated usefulness in biological data analysis.

Abstract

Researchers in the biological sciences nowadays often encounter the curse of high-dimensionality, which many previously developed statistical models fail to overcome. To tackle this problem, sufficient dimension reduction aims to estimate the central subspace (CS), in which all the necessary information supplied by the covariates regarding the response of interest is contained. Subsequent statistical analysis can then be made in a lower-dimensional space while preserving relevant information. Oftentimes studies are interested in a certain transformation of the response (the induced response), instead of the original one, whose corresponding CS may vary. When estimating the CS of the induced response, existing dimension reduction methods may, however, suffer the problem of inefficiency. In this article, we propose a more efficient two-stage estimation procedure to estimate the CS of an induced response. This approach is further extended to the case of censored responses. An application for combining multiple biomarkers is also illustrated. Simulation studies and two data examples provide further evidence of the usefulness of the proposed method.