Maximum likelihood estimation for multivariate normal samples : theory and methods

Show simple item record Strydom, H.F. (Hendrina Fredrika) Crowther, N.A.S. (Nicolaas Andries Sadie), 1944- 2012-03-15T06:34:09Z 2012-03-15T06:34:09Z 2012
dc.description.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. en
dc.description.librarian nf2012 en
dc.description.uri en_US
dc.identifier.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. en
dc.identifier.issn 0038-271X
dc.language.iso en en_US
dc.publisher South African Statistical Association en_US
dc.rights South African Statistical Association en
dc.subject Behrens-Fisher en
dc.subject Canonical statistic en
dc.subject Maximum likelihood (ML) estimation en
dc.subject Multivariate normal samples en
dc.subject Parameter structures en
dc.subject Proportional covariance matrices en
dc.subject Toeplitz correlation structure en
dc.subject.lcsh Exponential families (Statistics) en
dc.subject.lcsh Toeplitz matrices en
dc.title Maximum likelihood estimation for multivariate normal samples : theory and methods en
dc.type Article en

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