A Link-Free Approach for Testing Common Indices for Three or More Multi-Index Models
Liu et al. (2015) proposed a novel link-free procedure for testing whether two multi-index models share identical indices via the sufficient dimension reduction approach. However, their method can only be applied to data with two populations. In practice, we often deal with situations where the same variables are being measured on objects from three or more groups, and we would like to know how similar these groups are with respect to some overall features. In this paper, we propose a link-free method which could test if three or more multi-index models share the same indices. The asymptotic properties of our test statistic are developed. Numerical studies and a real data analysis are conducted to illustrate the performance of our method.
X. Liu et al., "A Link-Free Approach for Testing Common Indices for Three or More Multi-Index Models," Journal of Multivariate Analysis, vol. 153, pp. 236-245, Elsevier, Jan 2017.
The definitive version is available at https://doi.org/10.1016/j.jmva.2016.10.002
Mathematics and Statistics
Keywords and Phrases
Common principal component analysis; Multi-index model; Multiple populations; Sufficient dimension reduction
International Standard Serial Number (ISSN)
Article - Journal
© 2017 Elsevier, All rights reserved.
01 Jan 2017