Title

On Testing Common Indices for Two Multi-Index Models: A Link-Free Approach

Abstract

We propose a link-free procedure for testing whether two multi-index models share identical indices via the sufficient dimension reduction approach. Test statistics are developed based upon three different sufficient dimension reduction methods: (i) sliced inverse regression, (ii) sliced average variance estimation and (iii) directional regression. The asymptotic null distributions of our test statistics are derived. Monte Carlo studies are performed to investigate the efficacy of our proposed methods. A real-world application is also considered.

Department(s)

Mathematics and Statistics

Comments

The authors would like to thank Dr. James Schott for sending them the technical supplement to his paper [21] . Zhou Yu is supported by NSFC grants (No. 11201151 ), Program of Shanghai Subject Chief Scientist (14XD1401600) and the 111 Project (B14019).

Keywords and Phrases

Directional regression; Multi-index models; Sliced average variance estimation; Sliced inverse regression; Sufficient dimension reduction

International Standard Serial Number (ISSN)

0047-259X

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2015 Elsevier, All rights reserved.

Publication Date

01 Apr 2015

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