Please use this identifier to cite or link to this item: https://doi.org/10.1093/biomet/ass014
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dc.titlePenalized empirical likelihood and growing dimensional general estimating equations
dc.contributor.authorLeng, C.
dc.contributor.authorTang, C.Y.
dc.date.accessioned2014-10-28T05:14:15Z
dc.date.available2014-10-28T05:14:15Z
dc.date.issued2012-09
dc.identifier.citationLeng, 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
dc.identifier.issn00063444
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/105296
dc.description.abstractWhen 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.
dc.sourceScopus
dc.subjectEmpirical likelihood
dc.subjectGeneral estimating equation
dc.subjectHigh-dimensional data analysis
dc.subjectPenalized likelihood
dc.subjectVariable selection
dc.typeArticle
dc.contributor.departmentSTATISTICS & APPLIED PROBABILITY
dc.description.doi10.1093/biomet/ass014
dc.description.sourcetitleBiometrika
dc.description.volume99
dc.description.issue3
dc.description.page703-716
dc.description.codenBIOKA
dc.identifier.isiut000308019700013
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