Annals of Statistics

Dimension reduction for nonelliptically distributed predictors

Bing Li and Yuexiao Dong

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Sufficient dimension reduction methods often require stringent conditions on the joint distribution of the predictor, or, when such conditions are not satisfied, rely on marginal transformation or reweighting to fulfill them approximately. For example, a typical dimension reduction method would require the predictor to have elliptical or even multivariate normal distribution. In this paper, we reformulate the commonly used dimension reduction methods, via the notion of “central solution space,” so as to circumvent the requirements of such strong assumptions, while at the same time preserve the desirable properties of the classical methods, such as $\sqrt{n}$-consistency and asymptotic normality. Imposing elliptical distributions or even stronger assumptions on predictors is often considered as the necessary tradeoff for overcoming the “curse of dimensionality,” but the development of this paper shows that this need not be the case. The new methods will be compared with existing methods by simulation and applied to a data set.

Article information

Ann. Statist., Volume 37, Number 3 (2009), 1272-1298.

First available in Project Euclid: 10 April 2009

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 62H12: Estimation 62G08: Nonparametric regression 62G09: Resampling methods

Canonical correlation central solution spaces kernel inverse regression inverse regression sliced inverse regression parametric inverse regression


Li, Bing; Dong, Yuexiao. Dimension reduction for nonelliptically distributed predictors. Ann. Statist. 37 (2009), no. 3, 1272--1298. doi:10.1214/08-AOS598.

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