An exact test for equality of two normal mean vectors with monotone missing data

Year 2022, , 1211 - 1218, 01.08.2022

Jianqi Yu , Bin Wang , Tao Zhang

https://doi.org/10.15672/hujms.871588

Abstract

The problem of testing equality of two normal mean vectors with incomplete data when the covariance matrices are equal is considered. For data matrices with monotone missing pattern, an exact test is proposed as an alternative one to the traditional likelihood ration test. Numerical power comparisons show that the powers of the proposed test and the likelihood ration test are comparable. However, the proposed test is an exact one. It is easy to use and useful to identify the component that caused the rejection of null hypothesis. It is illustrated using an example.

Keywords

Monotone missing data, Likelihood ration test, Normal mean vector

Supporting Institution

NSFC

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