Penalized empirical likelihood and growing dimensional general estimating equations

• 成果类型: Article
• 署名作者: Leng, Chenlei; Tang, Cheng Yong
• 署名单位: National University of Singapore
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/ass014
• 发表日期: 2012
• 页码: 703716
• 关键词: VARIABLE SELECTION; diverging number; quasi-likelihood; adaptive lasso; shrinkage

摘要

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.