TWO-SAMPLE BEHRENS-FISHER PROBLEM FOR HIGH-DIMENSIONAL DATA

被引：43
|
作者：
Feng, Long [1 ]
Zou, Changliang [1 ]
Wang, Zhaojun [1 ]
Zhu, Lixing [2 ]
机构：
[1] Nankai Univ, Inst Stat, Tianjin 300071, Peoples R China
[2] Hong Kong Baptist Univ, Dept Math, Hong Kong, Hong Kong, Peoples R China
来源：
STATISTICA SINICA | 2015年 / 25卷 / 04期
关键词：
Asymptotic normality; Behrens-Fisher problem; high-dimensional data; large-p-small-n; two-sample test; MEAN VECTOR; SAMPLES;
D O I：
10.5705/ss.2014.048
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
This article is concerned with the two-sample Behrens-Fisher problem in high-dimensional settings. A test is proposed that is scale-invariant, asymptotically normal under certain mild conditions, and the dimensionality is allowed to grow in the rate, respectively, from square to cube of the sample size in different scenarios. We explain the necessity of bias correction for existing scale-invariant tests. We also give some examples to show the advantage of the scale-invariant test over scale-variant tests when variances of the two samples are different.
引用
收藏
页码：1297 / 1312
页数：16
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