Open Access
June 2016 A test for subvector of mean vector with two-step monotone missing data
Tamae Kawasaki, Takashi Seo
Author Affiliations +
SUT J. Math. 52(1): 21-39 (June 2016). DOI: 10.55937/sut/1469055995

Abstract

In this paper, we consider the one-sample problem of testing for the subvector of a mean vector with two-step monotone missing data. In the case that the data set consists of complete data with p(=p1+p2+p3) dimensions and incomplete data with (p1+p2) dimensions, we derive the likelihood ratio criterion for testing the (p2+p3) mean vector under the given mean vector of p1 dimensions. Furthermore, we propose an approximation for the upper percentile of the likelihood ratio test (LRT) statistic. We investigate the accuracy and asymptotic behavior of this approximation using Monte Carlo simulation. An example is presented in order to illustrate the method.

Funding Statement

Second author’s research was in part supported by Grant-in-Aid for Scientific Research (C) (26330050).

Acknowledgments

The authors would like to thank the referee for helpful comments and suggestions.

Citation

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Tamae Kawasaki. Takashi Seo. "A test for subvector of mean vector with two-step monotone missing data." SUT J. Math. 52 (1) 21 - 39, June 2016. https://doi.org/10.55937/sut/1469055995

Information

Received: 19 November 2015; Revised: 19 May 2016; Published: June 2016
First available in Project Euclid: 8 June 2022

Digital Object Identifier: 10.55937/sut/1469055995

Subjects:
Primary: 62H10 , 62H20

Keywords: likelihood ratio test , maximum likelihood estimators , Monte Carlo simulation , Rao’s U statistic

Rights: Copyright © 2016 Tokyo University of Science

Vol.52 • No. 1 • June 2016
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