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December 2011 Sufficient dimension reduction based on an ensemble of minimum average variance estimators
Xiangrong Yin, Bing Li
Ann. Statist. 39(6): 3392-3416 (December 2011). DOI: 10.1214/11-AOS950

Abstract

We introduce a class of dimension reduction estimators based on an ensemble of the minimum average variance estimates of functions that characterize the central subspace, such as the characteristic functions, the Box–Cox transformations and wavelet basis. The ensemble estimators exhaustively estimate the central subspace without imposing restrictive conditions on the predictors, and have the same convergence rate as the minimum average variance estimates. They are flexible and easy to implement, and allow repeated use of the available sample, which enhances accuracy. They are applicable to both univariate and multivariate responses in a unified form. We establish the consistency and convergence rate of these estimators, and the consistency of a cross validation criterion for order determination. We compare the ensemble estimators with other estimators in a wide variety of models, and establish their competent performance.

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Xiangrong Yin. Bing Li. "Sufficient dimension reduction based on an ensemble of minimum average variance estimators." Ann. Statist. 39 (6) 3392 - 3416, December 2011. https://doi.org/10.1214/11-AOS950

Information

Published: December 2011
First available in Project Euclid: 5 March 2012

zbMATH: 1246.62141
MathSciNet: MR3012413
Digital Object Identifier: 10.1214/11-AOS950

Subjects:
Primary: 62B05 , 62G08
Secondary: 62H12

Keywords: Central mean subspace , central subspace , Characteristic function , characterizing family , gradient estimation , projective resampling , Wavelets

Rights: Copyright © 2011 Institute of Mathematical Statistics

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Vol.39 • No. 6 • December 2011
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