Two-sample inference for normal mean vectors based on monotone missing data

DOI10.1016/j.jmva.2006.07.002zbMath1101.62048OpenAlexW2088919421MaRDI QIDQ853947

Maruthy K. Pannala, K. Krishnamoorthy, Jianqi Yu

Publication date: 7 December 2006

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmva.2006.07.002

zbMATH Keywords

incomplete datacoverage probabilitymaximum likelihood estimatorspowerssizesmoment approximation

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Hypothesis testing in multivariate analysis (62H15) Monte Carlo methods (65C05) Paired and multiple comparisons; multiple testing (62J15)

Related Items

Testing equality of mean vectors in a one-way MANOVA with monotone missing data ⋮ Testing equality of two normal covariance matrices with monotone missing data ⋮ An asymptotic approximation for EPMC in linear discriminant analysis based on two-step monotone missing samples ⋮ EPEM: efficient parameter estimation for multiple class monotone missing data ⋮ Testing equality of two mean vectors with monotone incomplete data ⋮ Test for high-dimensional mean vector under missing observations ⋮ Bias correction forT²type statistic with two-step monotone missing data ⋮ Multivariate Behrens-Fisher problem with missing data ⋮ An exact test for equality of two normal mean vectors with monotone missing data

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