Kernel dimension reduction in regression

TL;DR

This paper introduces a novel kernel-based method for sufficient dimension reduction in regression that leverages conditional covariance operators, avoiding restrictive assumptions and demonstrating competitive empirical performance.

Contribution

The paper develops a new kernel-based SDR methodology using conditional covariance operators, providing a consistent estimator without requiring linearity or ellipticity assumptions.

Findings

Estimator is consistent under weak conditions

Method is competitive in empirical tests

Does not rely on linearity or ellipticity assumptions

Abstract

We present a new methodology for sufficient dimension reduction (SDR). Our methodology derives directly from the formulation of SDR in terms of the conditional independence of the covariate $X$ from the response $Y$, given the projection of $X$ on the central subspace [cf. J. Amer. Statist. Assoc. 86 (1991) 316--342 and Regression Graphics (1998) Wiley]. We show that this conditional independence assertion can be characterized in terms of conditional covariance operators on reproducing kernel Hilbert spaces and we show how this characterization leads to an $M$-estimator for the central subspace. The resulting estimator is shown to be consistent under weak conditions; in particular, we do not have to impose linearity or ellipticity conditions of the kinds that are generally invoked for SDR methods. We also present empirical results showing that the new methodology is competitive in practice.