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Activity Number: 605
Type: Contributed
Date/Time: Thursday, August 6, 2009 : 10:30 AM to 12:20 PM
Sponsor: Section on Nonparametric Statistics
Abstract - #303744
Title: Central solution space for dimension reduction
Author(s): Yuexiao Dong*+ and Bing Li
Companies: Penn State University and Penn State University
Address: 429 Oakwood Ave, State College, PA, 16803,
Keywords: Central solution space ; Elliptical distribution ; Sliced average variance estimation ; Sliced inverse regression
Abstract:

Conventional dimension reduction methods, especially those based on inverse conditional moments, require the predictors to have elliptical or even multivariate normal distribution, or at least to satisfy a linearity condition. The notion of the central solution space can be used to circumvent such limitations. Generalization of the first-order methods, such as sliced inverse regression, has been studied by Li and Dong (2009). In this paper we generalize this idea to the second-order methods. Simulation studies show a substantial improvement of the modified methods over their classical counterparts.

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