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.
Recommended Citation
X. Liu et al., "On Testing Common Indices for Two Multi-Index Models: A Link-Free Approach," Journal of Multivariate Analysis, vol. 136, pp. 75 - 85, Elsevier, Apr 2015.
The definitive version is available at https://doi.org/10.1016/j.jmva.2015.01.009
Department(s)
Mathematics and Statistics
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
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).