A penalized empirical likelihood method in high dimensions
Soumendra N. Lahiri, Subhadeep Mukhopadhyay
The Annals of Statistics · 2012 · Volume 40 · Issue 5
Abstract
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.
Keywords
62E20, 62G09, 62G20, asymptotic distribution, long-range dependence, regularization, Rosenblatt process, simultaneous tests, subsampling, Wiener–Itô integral
Enable JavaScript for citation tools and additional paper interactions.