Please use this identifier to cite or link to this item: https://doi.org/10.1093/biomet/ass014
Title: Penalized empirical likelihood and growing dimensional general estimating equations
Authors: Leng, C. 
Tang, C.Y. 
Keywords: Empirical likelihood
General estimating equation
High-dimensional data analysis
Penalized likelihood
Variable selection
Issue Date: Sep-2012
Citation: Leng, C., Tang, C.Y. (2012-09). Penalized empirical likelihood and growing dimensional general estimating equations. Biometrika 99 (3) : 703-716. ScholarBank@NUS Repository. https://doi.org/10.1093/biomet/ass014
Abstract: 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. © 2012 Biometrika Trust.
Source Title: Biometrika
URI: http://scholarbank.nus.edu.sg/handle/10635/105296
ISSN: 00063444
DOI: 10.1093/biomet/ass014
Appears in Collections:Staff Publications

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