Please use this identifier to cite or link to this item:
https://doi.org/10.1093/biomet/asq057
DC Field | Value | |
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dc.title | Penalized high-dimensional empirical likelihood | |
dc.contributor.author | Tang, C.Y. | |
dc.contributor.author | Leng, C. | |
dc.date.accessioned | 2014-10-28T05:14:15Z | |
dc.date.available | 2014-10-28T05:14:15Z | |
dc.date.issued | 2010-12 | |
dc.identifier.citation | Tang, C.Y., Leng, C. (2010-12). Penalized high-dimensional empirical likelihood. Biometrika 97 (4) : 905-920. ScholarBank@NUS Repository. https://doi.org/10.1093/biomet/asq057 | |
dc.identifier.issn | 00063444 | |
dc.identifier.uri | http://scholarbank.nus.edu.sg/handle/10635/105297 | |
dc.description.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. | |
dc.source | Scopus | |
dc.subject | Confidence region | |
dc.subject | Empirical likelihood | |
dc.subject | High-dimensional data analysis | |
dc.subject | Penalized likelihood | |
dc.subject | Smoothly clipped absolute deviation | |
dc.subject | Variable selection | |
dc.type | Article | |
dc.contributor.department | STATISTICS & APPLIED PROBABILITY | |
dc.description.doi | 10.1093/biomet/asq057 | |
dc.description.sourcetitle | Biometrika | |
dc.description.volume | 97 | |
dc.description.issue | 4 | |
dc.description.page | 905-920 | |
dc.description.coden | BIOKA | |
dc.identifier.isiut | 000285194900010 | |
Appears in Collections: | Staff Publications |
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