Test function: A new approach for covering the central subspace

TL;DR

The paper introduces the test function (TF), a comprehensive methodology for sufficient dimension reduction that uses nonlinear transformations of the response to estimate the central subspace more effectively under weaker conditions.

Contribution

It presents a novel family of methods for estimating the central subspace using nonlinear response transformations, with theoretical guarantees and optimal plug-in estimators.

Findings

Two classes of TF methods based on conditional moments

Conditions for exhaustive estimation of the central subspace

Simulation results demonstrating effectiveness

Abstract

In this paper we offer a complete methodology for sufficient dimension reduction called the test function (TF). TF provides a new family of methods for the estimation of the central subspace (CS) based on the introduction of a nonlinear transformation of the response. Theoretical background of TF is developed under weaker conditions than the existing methods. By considering order 1 and 2 conditional moments of the predictor given the response, we divide TF in two classes. In each class we provide conditions that guarantee an exhaustive estimation of the CS. Besides, the optimal members are calculated via the minimization of the asymptotic mean squared error deriving from the distance between the CS and its estimate. This leads us to two plug-in methods which are evaluated with several simulations.