Missing data in the one-population multivariate normal patterned mean and covariance matrix testing and estimation problem

DOI10.1214/aos/1176346260zbMath0524.62050OpenAlexW2003590127MaRDI QIDQ584862

Ted H. Szatrowski

Publication date: 1983

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1214/aos/1176346260

zbMATH Keywords

covariance matrixdelta methodmissing dataNewton-Raphsonasymptotically normalEM algorithmsasymptotic joint distribution of maximum likelihood estimatesasymptotically chi-squaredgeneral one-population hypothesesmethod of scoringmultivariate normal linear patterned meannonnull distributions of likelihood ratio tests

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Asymptotic distribution theory in statistics (62E20) Hypothesis testing in multivariate analysis (62H15)

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Structured Modeling for Post-Mortem Brain Tissue Data ⋮ Applications of the matrix operators vech and vec ⋮ Missing data in the k-population multivariate normal patterned mean and covariance matrix testing and estimation problem ⋮ Relative efficiencies of estimates using patterned covariances or correlations in the multivariate normal estimation problem ⋮ ML estimation for the multivariate normal distribution with general linear model mea1 and linear-structure covariance matrix; one-population, complete-data case ⋮ Maximum likelihood estimation of the correlation coefficient in a bivariate normal model with missing data ⋮ Asymptotic distributions in the testing and estimation of the missing- data multivariate normal linear patterned mean and correlation matrix ⋮ Estimation for structural equation models with missing data

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