On Model-Free Conditional Coordinate Tests For Regressions

Editor(s)

de Leeuw, J.

Abstract

Existing model-free tests of the conditional coordinate hypothesis in sufficient dimension reduction (Cook (1998) [3]) focused mainly on the first-order estimation methods such as the sliced inverse regression estimation (Li (1991) [14]). Such testing procedures based on quadratic inference functions are difficult to be extended to second-order sufficient dimension reduction methods such as the sliced average variance estimation (Cook and Weisberg (1991) [9]). In this article, we develop two new model-free tests of the conditional predictor hypothesis. Moreover, our proposed test statistics can be adapted to commonly used sufficient dimension reduction methods of eigendecomposition type. We derive the asymptotic null distributions of the two test statistics and conduct simulation studies to examine the performances of the tests.

Department(s)

Mathematics and Statistics

Keywords and Phrases

Conditional Coordinate Test; Sufficient Dimension Reduction; Sliced Inverse Regression

International Standard Serial Number (ISSN)

0047-259X

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2012 Elsevier, All rights reserved.

Publication Date

01 Jan 2012

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