Improved simplified T2 test statistics for a mean vector with monotone missing data

DOI10.1080/03610918.2017.1419261OpenAlexW2783060367MaRDI QIDQ5085952

Ayaka Yagi, Takashi Seo, Zofia Hanusz

Publication date: 30 June 2022

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2017.1419261

zbMATH Keywords

asymptotic expansionMonte Carlo simulationmaximum likelihood estimatorBartlett correctionchi-squared approximation

Mathematics Subject Classification ID

Multivariate distribution of statistics (62H10) Asymptotic distribution theory in statistics (62E20)

Related Items (3)

Effect of nonnormality on tests for a mean vector with missing data under an elliptically contoured pattern-mixture model ⋮ Testing equality of two mean vectors with monotone incomplete data ⋮ Multivariate normality test based on kurtosis with two-step monotone missing data

Cites Work

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• Finite-sample inference with monotone incomplete multivariate normal data. I.
• Transformations with improved chi-squared approximations
• Maximum likelihood estimation for multivariate normal distribution with monotone sample
• Some Basic Properties of the Mle's for a Multivariate Normal Distribution with Monotone Missing Data
• Confidence estimation of a normal mean vector with incomplete data
• Finite-sample inference with monotone incomplete multivariate normal data, III: Hotelling’s T²-statistic
• Bias correction forT²type statistic with two-step monotone missing data
• Multivariate Statistics

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