Statistics and Its Interface
Volume 14 (2021)
Penalized empirical likelihood for high-dimensional generalized linear models
Pages: 83 – 94
We develop penalized empirical likelihood for parameter estimation and variable selection in high-dimensional generalized linear models. By using adaptive lasso penalty function, we show that the proposed estimator has the oracle property. Also, we consider the problem of testing hypothesis, and show that the nonparametric profiled empirical likelihood ratio statistic has asymptotic chi-square distribution. Some simulations and an application are given to illustrate the performance of the proposed method.
penalized empirical likelihood, high-dimensional data, variable selection, generalized linear model
2010 Mathematics Subject Classification
This research is supported by the Youth Fund for Humanities and Social Sciences Research of Ministry of Education (No. 18YJC910003), the Natural Science Basic Research Plan in Shaanxi Province of China (No. 2020JM-276) and the Fundamental Research Funds for the Central Universities (No. GK201901008).
Received 9 August 2019
Accepted 5 April 2020
Published 22 December 2020