Statistics and Its Interface
Volume 5 (2012)
On the Mahalanobis-distance based penalized empirical likelihood method in high dimensions
Pages: 331 – 338
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.
asymptotic null distribution, empirical likelihood, high dimension, regularization, simultaneous tests