Title: Penalized jackknife empirical likelihood in high dimensions
Authors: Na Zhao - The University of HongKong (Hong Kong) [presenting]
Jinfeng Xu - University of Hong Kong (Hong Kong)
Abstract: Jackknife empirical likelihood(JEL) is an attractive approach for statistical inferences with nonlinear statistics such as U-statistics. However, most contemporary problem involving high dimension model selection and the number of parameters diverges to infinity. We propose a penalized JEL method which preserves the main advantages of JEL and leads to reliable variable selection based on the estimating equations with U-statistic structure in the high-dimensional framework. Under certain regularity conditions, we establish the asymptotic theory and oracle property for the JEL and its penalized version when the number of estimating equations and parameters may increase as the sample size increases. Simulation studies were carried out to examine the performance of the proposed methods, we also apply our method to real data sets and obtain encouraging results.