Journal
JOURNAL OF MULTIVARIATE ANALYSIS
Volume 98, Issue 10, Pages 1923-1954Publisher
ELSEVIER INC
DOI: 10.1016/j.jmva.2007.05.005
Keywords
semiparametric model; empirical likelihood; penalization
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We propose an empirical likelihood-based estimation method for conditional estimating equations containing unknown functions, which can be applied for various semiparametric models. The proposed method is based on the methods of conditional empirical likelihood and penalization. Thus, our estimator is called the penalized empirical likelihood (PEL) estimator. For the whole parameter including infinite-dimensional unknown functions, we derive the consistency and a convergence rate of the PEL estimator. Furthermore, for the finite-dimensional parametric component, we show the asymptotic normality and efficiency of the PEL estimator. We illustrate the theory by three examples. Simulation results show reasonable finite sample properties of our estimator. (c) 2007 Elsevier Inc. All rights reserved.
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