Effects of data dimension on empirical likelihood

被引：82

作者：

Chen, Song Xi ^{[1
]}

Peng, Liang ^{[2
]}

Qin, Ying-Li ^{[1
]}

机构：

[1] Iowa State Univ, Dept Stat, Ames, IA 50011 USA

[2] Georgia Inst Technol, Sch Math, Atlanta, GA 30332 USA

来源：

BIOMETRIKA | 2009年 / 96卷 / 03期

基金：

美国国家科学基金会;

关键词：

Asymptotic normality; Data dimension; Empirical likelihood; High-dimensional data; CONFIDENCE-INTERVALS; TESTS;

D O I：

10.1093/biomet/asp037

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

We evaluate the effects of data dimension on the asymptotic normality of the empirical likelihood ratio for high-dimensional data under a general multivariate model. Data dimension and dependence among components of the multivariate random vector affect the empirical likelihood directly through the trace and the eigenvalues of the covariance matrix. The growth rates to infinity we obtain for the data dimension improve the rates of Hjort et al. (2008).

引用

页码：711 / 722

页数：12

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[2]

[Anonymous], 2003, Introduction to Nessus

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Bai ZD, 1996, STAT SINICA, V6, P311

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