A one-sample location test based on weighted averaging of two test statistics when the dimension and the sample size are large. Issue 7 (3rd April 2017) - Hyodo, Masashi; Nishiyama, Takahiro
ABSTRACT: We discuss a one-sample location test that can be used when the dimension and the sample size are large. It is well-known that the power of Hotelling's test decreases when the dimension is close to the sample size. To address this loss of power, some non exact approaches were proposed, e.g., Dempster (1958, 1960 ), Bai and Saranadasa (1996 ), and Srivastava and Du (2008 ). In this article, we focus on Hotelling's test and Dempster's test. The comparative merits and demerits of these two tests vary according to the local parameters. In particular, we consider the situation where it is difficult to determine which test should be used, that is, where the two tests are asymptotically equivalent in terms of local power. We propose a new statistic based on the weighted averaging of Hotelling's T 2 -statistic and Dempster's statistic that can be applied in such a situation. Our weight is determined on the basis of the maximum local asymptotic power on a restricted parameter space that induces local asymptotic equivalence between Hotelling's test and Dempster's test. Numerical results show that our test is more stable than Hotelling's T 2 -statistic and Dempster's statistic in most parameter settings.