Finite-sample inference with monotone incomplete multivariate normal data, III: Hotelling’s T2-statistic

DOI10.1177/1471082X13494611OpenAlexW2169867951MaRDI QIDQ4970828

Megan M. Romer, Donald St. P. Richards

Publication date: 7 October 2020

Published in: Statistical Modelling (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1177/1471082x13494611

zbMATH Keywords

asymptotic distributionconfidence regionmaximum likelihood estimatorequivariancestochastic representationmissing datasimultaneous confidence intervalsmultivariate normal distributionmissing completely at randompivotal quantityorthogonal invariancemonotone incomplete dataHotelling's \(T^2\)-squared statistic

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (4)

Effect of nonnormality on tests for a mean vector with missing data under an elliptically contoured pattern-mixture model ⋮ Improved simplified T² test statistics for a mean vector with monotone missing data ⋮ Testing equality of two mean vectors with monotone incomplete data ⋮ Kurtosis tests for multivariate normality with monotone incomplete data

Uses Software

• R
• MINITAB

Cites Work

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• Kurtosis tests for multivariate normality with monotone incomplete data
• Finite-sample inference with monotone incomplete multivariate normal data. I.
• Finite-sample inference with monotone incomplete multivariate normal data. II
• The Stein phenomenon for monotone incomplete multivariate normal data
• Maximum likelihood estimation of the mean of a multivariate normal population with monotone incomplete data
• Maximum-likelihood estimation of the parameters of a multivariate normal distribution
• Maximum likelihood estimation for multivariate normal distribution with monotone sample
• Maximum Likelihood Estimates for a Multivariate Normal Distribution when some Observations are Missing
• Inference about means from incomplete multivariate data
• Confidence estimation of a normal mean vector with incomplete data

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