Please use this identifier to cite or link to this item:
Title: Penalized high-dimensional empirical likelihood
Authors: Tang, C.Y. 
Leng, C. 
Keywords: Confidence region
Empirical likelihood
High-dimensional data analysis
Penalized likelihood
Smoothly clipped absolute deviation
Variable selection
Issue Date: Dec-2010
Citation: Tang, C.Y., Leng, C. (2010-12). Penalized high-dimensional empirical likelihood. Biometrika 97 (4) : 905-920. ScholarBank@NUS Repository.
Abstract: We propose penalized empirical likelihood for parameter estimation and variable selection for problems with diverging numbers of parameters. Our results are demonstrated for estimating the mean vector in multivariate analysis and regression coefficients in linear models. By using an appropriate penalty function, we show that penalized empirical likelihood has the oracle property. That is, with probability tending to 1, penalized empirical likelihood identifies the true model and estimates the nonzero coefficients as efficiently as if the sparsity of the true model was known in advance. The advantage of penalized empirical likelihood as a nonparametric likelihood approach is illustrated by testing hypotheses and constructing confidence regions. Numerical simulations confirm our theoretical findings. © 2010 Biometrica Trust.
Source Title: Biometrika
ISSN: 00063444
DOI: 10.1093/biomet/asq057
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.