Multivariate Behrens–Fisher problem with missing data

K. Krishnamoorthy and Jianqi Yu

Journal of Multivariate Analysis, 2012, vol. 105, issue 1, 141-150

Abstract: Inference about the difference between two normal mean vectors when the covariance matrices are unknown and arbitrary is considered. Assuming that the incomplete data are of monotone pattern, a pivotal quantity, similar to the Hotelling T2 statistic, is proposed. A satisfactory moment approximation to the distribution of the pivotal quantity is derived. Hypothesis testing and confidence estimation based on the approximate distribution are outlined. The accuracy of the approximation is investigated using Monte Carlo simulation. Monte Carlo studies indicate that the approximate method is very satisfactory even for moderately small samples. The proposed methods are illustrated using an example.

Keywords: Approximate degrees of freedom solution; Coverage probability; Incomplete data; Maximum likelihood estimators; Moment approximation; Sizes (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (3)

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DOI: 10.1016/j.jmva.2011.08.019

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