Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 안재윤 | - |
dc.date.accessioned | 2016-08-27T04:08:48Z | - |
dc.date.available | 2016-08-27T04:08:48Z | - |
dc.date.issued | 2016 | - |
dc.identifier.issn | 0943-4062 | - |
dc.identifier.issn | 1613-9658 | - |
dc.identifier.other | OAK-15302 | - |
dc.identifier.uri | https://dspace.ewha.ac.kr/handle/2015.oak/217402 | - |
dc.description.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. | - |
dc.language | English | - |
dc.publisher | SPRINGER HEIDELBERG | - |
dc.subject | High dimension | - |
dc.subject | Two sample mean test | - |
dc.subject | Coefficient vector | - |
dc.subject | Data adaptive | - |
dc.title | On high-dimensional two sample mean testing statistics: a comparative study with a data adaptive choice of coefficient vector | - |
dc.type | Article | - |
dc.relation.issue | 2 | - |
dc.relation.volume | 31 | - |
dc.relation.index | SCIE | - |
dc.relation.index | SCOPUS | - |
dc.relation.startpage | 451 | - |
dc.relation.lastpage | 464 | - |
dc.relation.journaltitle | COMPUTATIONAL STATISTICS | - |
dc.identifier.doi | 10.1007/s00180-015-0605-7 | - |
dc.identifier.wosid | WOS:000374375800003 | - |
dc.identifier.scopusid | 2-s2.0-84937129435 | - |
dc.author.google | Kim, Soeun | - |
dc.author.google | Ahn, Jae Youn | - |
dc.author.google | Lee, Woojoo | - |
dc.contributor.scopusid | 안재윤(36472886700;57329191200) | - |
dc.date.modifydate | 20230901081001 | - |