Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1153856 | Statistical Methodology | 2009 | 26 Pages |
Abstract
The coefficient of variation (CV) matrix for a pp-dimensional random vector is the covariance matrix scaled by the pp-vector of means such that the diagonal components are the squared coefficients of variation. In this article, principal component models for the CV matrix are proposed and least-squares estimators of the eigenvalues and eigenvectors are developed. The asymptotic joint distribution of the least-squares estimators is derived under general conditions. The proposed estimation and inference methods are illustrated using a real data set. The results of a small simulation study that examines the validity of the proposed inference procedures are reported.
Keywords
Related Topics
Physical Sciences and Engineering
Mathematics
Statistics and Probability
Authors
Robert J. Boik, Amin Shirvani,