A PENALIZED EMPIRICAL LIKELIHOOD METHOD IN HIGH DIMENSIONS

• 成果类型: Article
• 署名作者: Lahiri, Soumendra N.; Mukhopadhyay, Subhadeep
• 署名单位: Texas A&M University System; Texas A&M University College Station
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/12-AOS1040
• 发表日期: 2012
• 页码: 2511-2540
• 关键词: long-range dependence; Semiparametric models; confidence-intervals; ratio; CONVERGENCE

摘要

This paper formulates a penalized empirical likelihood (PEL) method for inference on the population mean when the dimension of the observations may grow faster than the sample size. Asymptotic distributions of the PEL ratio statistic is derived under different component-wise dependence structures of the observations, namely, (i) non-Ergodic, (ii) long-range dependence and (iii) short-range dependence. It follows that the limit distribution of the proposed PEL ratio statistic can vary widely depending on the correlation structure, and it is typically different from the usual chi-squared limit of the empirical likelihood ratio statistic in the fixed and finite dimensional case. A unified subsampling based calibration is proposed, and its validity is established in all three cases, (i)-(iii). Finite sample properties of the method are investigated through a simulation study.