On high-dimensional two sample mean testing statistics: a comparative study with a data adaptive choice of coefficient vector
- 주제(키워드) Coefficient vector , Data adaptive , High dimension , Two sample mean test
- 등재 SCIE, SCOPUS
- 발행기관 Springer Verlag
- 발행년도 2015
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000118412
- 본문언어 영어
- Published As http://dx.doi.org/10.1007/s00180-015-0605-7
- 저작권 이화여자대학교 논문은 저작권에 의해 보호받습니다.
초록/요약
The key issues involved in two sample tests in high dimensional problems arise due to large dimension of the mean vector for a relatively small sample size. Recently, Wang et al. (Stat Sin 23:667–690, 2013) proposed a jackknife empirical likelihood test that works under weak assumptions on the dimension of variables (p), and showed that the test statistic has a chi-square limit regardless of whether p is finite or diverges. The sufficient condition required for this statistic is still restrictive. In this paper we significantly relax the sufficient condition for the asymptotic chi-square limit with models allowing flexible dependence structures and derive simpler alternative statistics for testing the equality of two high dimensional means. The proposed statistics have a chi-squared distribution or the maximum of two independent chi-square statistics as their limiting distributions, and the asymptotic results hold for either finite or divergent p. We also propose a data-adaptive method to select the coefficient vector, and compare the various methods in simulation studies. The proposed choice of coefficient vector substantially increases power in the simulation. © 2015 Springer-Verlag Berlin Heidelberg
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