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Regularization parameter selection for penalized empirical likelihood estimator. (English) Zbl 1417.62187
Summary: Penalized estimation is a useful technique for variable selection when the number of candidate variables is large. A crucial issue in penalized estimation is the selection of the regularization parameter because the performance of the estimator largely depends on an appropriate choice. However, no theoretically sound selection method currently exists for the penalized estimation of moment restriction models. To address this important issue, we develop a novel information criterion, which we call the empirical likelihood information criterion, to select the regularization parameter of the penalized empirical likelihood estimator. The information criterion is derived as an estimator of the expected value of the Kullback-Leibler information criterion from an estimated model to the true data generating process.
MSC:
62J05 Linear regression; mixed models
62G05 Nonparametric estimation
62B10 Statistical aspects of information-theoretic topics
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