Maximum likelihood estimation for multivariate normal samples : theory and methods
Loading...
Date
Authors
Strydom, Hendrina Fredrika
Crowther, N.A.S. (Nicolaas Andries Sadie), 1944-
Journal Title
Journal ISSN
Volume Title
Publisher
South African Statistical Association
Abstract
Maximum likelihood estimation of parameter structures in the
case of multivariate normal samples is considered. The procedure provides
a new statistical methodology for maximum likelihood estimation which does
not require derivation and solution of the likelihood equations. It is a flexible
procedure for the analysis of specific structures in mean vectors and covariance
matrices – including the case where the sample size is small relative to the
dimension of the observations. Special cases include different variations of
the Behrens-Fisher problem, proportional covariancematrices and proportional
mean vectors. Specific structures are illustrated with real data examples.
Description
Keywords
Behrens-Fisher, Canonical statistic, Maximum likelihood (ML) estimation, Multivariate normal samples, Parameter structures, Proportional covariance matrices, Toeplitz correlation structure
Sustainable Development Goals
Citation
Strydom, HF & Crowther, NAS 2012, 'Maximum likelihood estimation for multivariate normal samples : theory and methods', South African Statistical Journal, vol. 46, no. 1, pp. 115-153.