ML estimation for the multivariate normal distribution with general linear model mea1 and linear-structure covariance matrix; one-population, complete-data case

DOI10.1080/03610928608829227zbMath0691.62051OpenAlexW2005793210MaRDI QIDQ3033138

Ronald W. Helms, Dalton F. Andrade

Publication date: 1986

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610928608829227

zbMATH Keywords

maximum likelihoodmultivariate normal distributioncomplete-data casegeneral linear model mean

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Asymptotic distribution theory in statistics (62E20)

Related Items (2)

Linear Equality Constraints in the General Linear Mixed Model ⋮ The growth curve model: a review

Cites Work

• Missing data in the one-population multivariate normal patterned mean and covariance matrix testing and estimation problem
• Asymptotic nonnull distributions for likelihood ratio statistics in the multivariate normal patterned mean and covariance matrix testing problem
• Explicit maximum likelihood estimates from balanced data in the mixed model of the analysis of variance
• Asymptotically efficient estimation of covariance matrices with linear structure
• Computation of variance components using the em algorithm
• Some likelihood ratio tests when a normal covariance matrix has certain reducible linear structures
• Explicit maximum likelihood estimators for certain patterned covariance matrices
• On Testing a Multivariate Linear Hypothesis When the Covariance Matrix and Its Inverse Have the Same Pattern
• Testing and Estimation for a Circular Stationary Model
• Linear Statistical Inference and its Applications
• Testing Compound Symmetry in a Normal Multivariate Distribution
• Sample Criteria for Testing Equality of Means, Equality of Variances, and Equality of Covariances in a Normal Multivariate Distribution

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