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

Authors:
Jianqi Yu;K. Krishnamoorthy;Maruthy K. Pannala
Affiliations:
Department of Mathematics, University of Louisiana at Lafayette, Lafayette, LA;Department of Mathematics, University of Louisiana at Lafayette, Lafayette, LA;Actuarial Department, United Guarantee Corporation, Greensboro, NC
Venue:
Journal of Multivariate Analysis
Year:
2006

Citing 2
Cited 3

Statistical analysis with missing data

Statistical analysis with missing data
Some basic properties of the MLE's for a multivariate normal distribution with monotone missing data

American Journal of Mathematical and Management Sciences - Special issue on MS1-2000 multivariate statistical analysis

An asymptotic approximation for EPMC in linear discriminant analysis based on two-step monotone missing samples

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Multivariate Behrens-Fisher problem with missing data

Journal of Multivariate Analysis
Comparison of confidence intervals for correlation coefficients based on incomplete monotone samples and those based on listwise deletion

Journal of Multivariate Analysis

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Abstract

Inferential procedures for the difference between two multivariate normal mean vectors based on incomplete data matrices with different monotone patterns are developed. Assuming that the population covariance matrices are equal, a pivotal quantity, similar to the Hotelling T2 statistic, is proposed, and its approximate distribution is derived. Hypothesis testing and confidence estimation of the difference between the mean vectors based on the approximate distribution are outlined. The validity of the approximation is investigated using Monte Carlo simulation. Monte Carlo studies indicate that the approximate method is very satisfactory even for small samples. A multiple comparison procedure is outlined and the proposed methods are illustrated using an example.