A constructive approach to the estimation of dimension reduction directions

The Annals of Statistics · 2007 · Volume 35 · Issue 6

Abstract

In this paper we propose two new methods to estimate the dimension-reduction directions of the central subspace (CS) by constructing a regression model such that the directions are all captured in the regression mean. Compared with the inverse regression estimation methods [e.g., J. Amer. Statist. Assoc. 86 (1991) 328–332, J. Amer. Statist. Assoc. 86 (1991) 316–342, J. Amer. Statist. Assoc. 87 (1992) 1025–1039], the new methods require no strong assumptions on the design of covariates or the functional relation between regressors and the response variable, and have better performance than the inverse regression estimation methods for finite samples. Compared with the direct regression estimation methods [e.g., J. Amer. Statist. Assoc. 84 (1989) 986–995, Ann. Statist. 29 (2001) 1537–1566, J. R. Stat. Soc. Ser. B Stat. Methodol. 64 (2002) 363–410], which can only estimate the directions of CS in the regression mean, the new methods can detect the directions of CS exhaustively. Consistency of the estimators and the convergence of corresponding algorithms are proved.

Keywords

62G08, 62G09, 62H05, Conditional density function, convergence of algorithm, double-kernel smoothing, efficient dimension reduction, root-n consistency

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