Journal
BIOMETRIKA
Volume 99, Issue 3, Pages 703-716Publisher
OXFORD UNIV PRESS
DOI: 10.1093/biomet/ass014
Keywords
Empirical likelihood; General estimating equation; High-dimensional data analysis; Penalized likelihood; Variable selection
Funding
- National University of Singapore
- National University of Singapore Risk Management Institute
Ask authors/readers for more resources
When a parametric likelihood function is not specified for a model, estimating equations may provide an instrument for statistical inference. Qin and Lawless (1994) illustrated that empirical likelihood makes optimal use of these equations in inferences for fixed low-dimensional unknown parameters. In this paper, we study empirical likelihood for general estimating equations with growing high dimensionality and propose a penalized empirical likelihood approach for parameter estimation and variable selection. We quantify the asymptotic properties of empirical likelihood and its penalized version, and show that penalized empirical likelihood has the oracle property. The performance of the proposed method is illustrated via simulated applications and a data analysis.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available