Testing parallelism and confidence intervals of level difference in an intraclass correlation model with monotone missing data

Yuichiro Saeki, Takashi Seo and Hiroto Hyakutake

Communications in Statistics - Theory and Methods, 2023, vol. 52, issue 17, 6147-6160

Abstract: In this article, we consider a parallelism test and confidence interval of level difference with uniform covariance structure wherein each dataset has a monotone missing data. A likelihood ratio test statistic is derived using the maximum likelihood estimators of the parameters for the transformed dataset of a contrast matrix. Furthermore, its exact null distribution is presented using a contrast transformation matrix. Moreover, an approximate confidence interval of level difference in two sample problem is presented under parallelism using the upper percentiles of Student’s t-distribution. Finally, a Monte Carlo simulation and a numerical example are given.

Date: 2023
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DOI: 10.1080/03610926.2022.2026961

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