Empirical likelihood test for high dimensional linear models

Liang Peng; Yongcheng Qi; Ruodu Wang

doi:10.1016/j.spl.2013.12.019

Empirical likelihood test for high dimensional linear models

Liang Peng, Yongcheng Qi, Ruodu Wang

Mathematics & Statistics

Research output: Contribution to journal › Article › peer-review

6 Scopus citations

Abstract

We propose an empirical likelihood method to test whether the coefficients in a possibly high-dimensional linear model are equal to given values. The asymptotic distribution of the test statistic is independent of the number of covariates in the linear model.

Original language	English (US)
Pages (from-to)	85-90
Number of pages	6
Journal	Statistics and Probability Letters
Volume	86
Issue number	1
DOIs	https://doi.org/10.1016/j.spl.2013.12.019
State	Published - Mar 2014

Bibliographical note

Funding Information:
We thank the reviewer for constructive comments, which weaken the conditions in Theorem 1 and simplify the proofs of Lemma 1 , Example 1 and Theorem 1 . Peng’s research was supported by the NSF Grant DMS-1005336 , Qi’s research was supported by the NSF Grant DMS-1005345 and Wang’s research was supported by the Natural Sciences and Engineering Research Council of Canada .

Keywords

Empirical likelihood
High-dimensional data
Hypothesis test
Linear model

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title = "Empirical likelihood test for high dimensional linear models",

abstract = "We propose an empirical likelihood method to test whether the coefficients in a possibly high-dimensional linear model are equal to given values. The asymptotic distribution of the test statistic is independent of the number of covariates in the linear model.",

keywords = "Empirical likelihood, High-dimensional data, Hypothesis test, Linear model",

author = "Liang Peng and Yongcheng Qi and Ruodu Wang",

note = "Funding Information: We thank the reviewer for constructive comments, which weaken the conditions in Theorem 1 and simplify the proofs of Lemma 1 , Example 1 and Theorem 1 . Peng{\textquoteright}s research was supported by the NSF Grant DMS-1005336 , Qi{\textquoteright}s research was supported by the NSF Grant DMS-1005345 and Wang{\textquoteright}s research was supported by the Natural Sciences and Engineering Research Council of Canada .",

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T1 - Empirical likelihood test for high dimensional linear models

AU - Peng, Liang

AU - Qi, Yongcheng

AU - Wang, Ruodu

N1 - Funding Information: We thank the reviewer for constructive comments, which weaken the conditions in Theorem 1 and simplify the proofs of Lemma 1 , Example 1 and Theorem 1 . Peng’s research was supported by the NSF Grant DMS-1005336 , Qi’s research was supported by the NSF Grant DMS-1005345 and Wang’s research was supported by the Natural Sciences and Engineering Research Council of Canada .

PY - 2014/3

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N2 - We propose an empirical likelihood method to test whether the coefficients in a possibly high-dimensional linear model are equal to given values. The asymptotic distribution of the test statistic is independent of the number of covariates in the linear model.

AB - We propose an empirical likelihood method to test whether the coefficients in a possibly high-dimensional linear model are equal to given values. The asymptotic distribution of the test statistic is independent of the number of covariates in the linear model.

KW - Empirical likelihood

KW - High-dimensional data

KW - Hypothesis test

KW - Linear model

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SP - 85

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JO - Statistics and Probability Letters

JF - Statistics and Probability Letters

IS - 1

ER -

Empirical likelihood test for high dimensional linear models

Abstract

Bibliographical note

Keywords

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