Title

Determining The Dimensionality In Sliced Inverse Regression

Authors

Authors

J. R. Schott

Comments

Authors: contact us about adding a copy of your work at STARS@ucf.edu

Abbreviated Journal Title

J. Am. Stat. Assoc.

Keywords

EIGENPROJECTION; ELLIPTICALLY SYMMETRICAL DISTRIBUTION; GENERAL; REGRESSION MODEL; PROJECTION MATRIX; REDUCTION; Statistics & Probability

Abstract

A general regression problem is one in which a response variable can be expressed as some function of one or more different linear combinations of a set of explanatory variables as well as a random error term. Sliced inverse regression is a method for determining these linear combinations. In this article we address the problem of determining how many linear combinations are involved . Procedures based on conditional means and conditional covariance matrices, as well as a procedure combining the two approaches, are considered. In each case we develop a test that has an asymptotic chi-squared distribution when the vector of explanatory variables is sampled from an elliptically symmetric distribution.

Journal Title

Journal of the American Statistical Association

Volume

89

Issue/Number

425

Publication Date

1-1-1994

Document Type

Article

Language

English

First Page

141

Last Page

148

WOS Identifier

WOS:A1994MY54600016

ISSN

0162-1459

Share

COinS