Empirical likelihood test for high dimensional linear models

Liang Peng, Yongcheng Qi, Ruodu Wang

Research output: Contribution to journalArticlepeer-review

4 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 languageEnglish (US)
Pages (from-to)85-90
Number of pages6
JournalStatistics and Probability Letters
Volume86
Issue number1
DOIs
StatePublished - 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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