Title

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.

Department(s)

Mathematics and Statistics

Keywords and Phrases

multivariate regression; dimension reduction; central subspaces; intra-cluster information; k-means clustering

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2009 International Press, All rights reserved.


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