Testing equality of two normal covariance matrices with monotone missing data. Issue 16 (17th August 2020)

• Record Type: Journal Article
• Authors: Yu, Jianqi; Krishnamoorthy, Kalimuthu; He, Yafei
• Is Part Of: Communications in statistics. Volume 49:Issue 16(2020)
• Page Start: 3911
• Page End: 3918
• Publication Date: 2020-08-17
• Subjects: Likelihood ratio test -- maximum likelihood estimators -- missing data -- monotone pattern -- powers -- sizes; 62H12 -- 62H15; Mathematical statistics -- Periodicals; Mathematics; Statistics; 519.2
• DOI: 10.1080/03610926.2019.1591453
• Languages: English
• ISSNs: 0361-0926

Abstract

Abstract: The problem of testing equality of two multivariate normal covariance matrices is considered. Assuming that the incomplete data are of monotone pattern, a quantity similar to the Likelihood Ratio Test Statistic is proposed. A satisfactory approximation to the distribution of the quantity is derived. Hypothesis testing based on the approximate distribution is outlined. The merits of the test are investigated using Monte Carlo simulation. Monte Carlo studies indicate that the test is very satisfactory even for moderately small samples. The proposed methods are illustrated using an example.