Uniformly more powerful tests for hypotheses about linear inequalities when the variance is unknown期刊界 All Journals 搜尽天下杂志传播学术成果专业期刊搜索期刊信息化学术搜索

Uniformly more powerful tests for hypotheses about linear inequalities when the variance is unknown

Authors:	Yining Wang Michael P. McDermott

Affiliation:	Schering-Plough Research Institute, 2015 Galloping Hill Road, K-15-2, 2315, Kenilworth, New Jersey 07033-0539 ; Department of Biostatistics, University of Rochester, 601 Elmwood Avenue, Box 630, Rochester, New York 14642

Abstract:	Let X be a -dimensional normal random vector with unknown mean and covariance matrix $Sigma =sigma ^{2}Sigma _{0}$ , where $Sigma _{0}$ is a known matrix and $sigma ^{2}$ an unknown parameter. This paper gives a test for the null hypothesis that lies either on the boundary or in the exterior of a closed, convex polyhedral cone versus the alternative hypothesis that lies in the interior of the cone. Our test is uniformly more powerful than the likelihood ratio test.

Keywords:	Conditional distribution likelihood ratio test one-sided testing polyhedral cone

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