A Minimum Discrepancy Approach to Multivariate Dimension Reduction via K-means Inverse Regression
Editor(s)
Zhang, Heping
Abstract
We proposed a new method to estimate the intra-cluster adjusted central subspace for regressions with multivariate responses. Following Setodji and Cook (2004), we made use of the k-means algorithm to cluster the observed response vectors. Our method was designed to recover the intracluster information and outperformed previous method with respect to estimation accuracies on both the central subspace and its dimension. It also allowed us to test the predictor effects in a model-free approach. Simulation and a real data example were given to illustrate our methodology.
Recommended Citation
X. M. Wen et al., "A Minimum Discrepancy Approach to Multivariate Dimension Reduction via K-means Inverse Regression," Statistics and Its Interface, International Press, Jan 2009.
The definitive version is available at https://doi.org/10.4310/SII.2009.v2.n4.a11
Department(s)
Mathematics and Statistics
Keywords and Phrases
multivariate regression; dimension reduction; central subspaces; intra-cluster information; k-means clustering
International Standard Serial Number (ISSN)
1938-7989
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2009 International Press, All rights reserved.
Publication Date
01 Jan 2009
