Article ID Journal Published Year Pages File Type
417175 Computational Statistics & Data Analysis 2008 10 Pages PDF
Abstract

Sufficient dimension reduction methodologies in regression have been developed in the past decade, focusing mostly on predictors. Here, we propose a methodology to reduce the dimension of the response vector in multivariate regression, without loss of information about the conditional mean. The asymptotic distributions of dimension test statistics are chi-squared distributions, and an estimate of the dimension reduction subspace is asymptotically efficient. Moreover, the proposed methodology enables us to test response effects for the conditional mean. Properties of the proposed method are studied via simulation.

Related Topics
Physical Sciences and Engineering Computer Science Computational Theory and Mathematics
Authors
, ,