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  • 标题:On the Mahalanobis-distance based penalized empirical likelihood method in high dimensions
  • 本地全文:下载
  • 作者:S. N. Lahiri ; S. Mukhopadhyay
  • 期刊名称:Statistics and Its Interface
  • 印刷版ISSN:1938-7989
  • 电子版ISSN:1938-7997
  • 出版年度:2012
  • 卷号:5
  • 期号:3
  • 页码:331-338
  • DOI:10.4310/SII.2012.v5.n3.a5
  • 出版社:International Press
  • 摘要:In this paper, we consider the penalized empirical likelihood (PEL) method of Bartolucci (2007) for inference on the population mean which is a modification of the standard empirical likelihood and employs a penalty based on the Mahalanobis-distance. We derive the asymptotic distributions of the PEL ratio statistic when the dimension of the observations increases with the sample size. Finite sample properties of the method are investigated through a small simulation study.
  • 关键词:asymptotic null distribution; empirical likelihood; high dimension; regularization; simultaneous tests
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