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On high-dimensional two sample mean testing statistics: a comparative study with a data adaptive choice of coefficient vector

Title
On high-dimensional two sample mean testing statistics: a comparative study with a data adaptive choice of coefficient vector
Authors
Kim, SoeunAhn, Jae YounLee, Woojoo
Ewha Authors
안재윤
SCOPUS Author ID
안재윤scopus
Issue Date
2016
Journal Title
COMPUTATIONAL STATISTICS
ISSN
0943-4062JCR Link1613-9658JCR Link
Citation
vol. 31, no. 2, pp. 451 - 464
Keywords
High dimensionTwo sample mean testCoefficient vectorData adaptive
Publisher
SPRINGER HEIDELBERG
Indexed
SCIE; SCOPUS WOS scopus
Abstract
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.
DOI
10.1007/s00180-015-0605-7
Appears in Collections:
자연과학대학 > 통계학전공 > Journal papers
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