Extended GMANOVA model with a linearly structured covariance matrix

In this paper we consider the extended generalized multivariate analysis of variance (GMANOVA) with a linearly structured covariance matrix. The main theme is to find explicit estimators for the mean and for the linearly structured covariance matrix.We show how to decompose the residual space, the o...

Full description

Saved in:
Bibliographic Details
Published in:Mathematical methods of statistics 2015-10, Vol.24 (4), p.280-291
Main Authors: Nzabanita, J., von Rosen, D., Singull, M.
Format: Article
Language:English
Subjects:
Mathematics and Statistics
Probability Theory and Statistics
Sannolikhetsteori och statistik
Statistical Theory and Methods
Statistics
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this paper we consider the extended generalized multivariate analysis of variance (GMANOVA) with a linearly structured covariance matrix. The main theme is to find explicit estimators for the mean and for the linearly structured covariance matrix.We show how to decompose the residual space, the orthogonal complement to the mean space, into m + 1 orthogonal subspaces and how to derive explicit estimators of the covariance matrix from the sum of squared residuals obtained by projecting observations on those subspaces. Also an explicit estimator of the mean is derived and some properties of the proposed estimators are studied.
ISSN:1066-5307
1934-8045
1934-8045
DOI:10.3103/S1066530715040031