Penalized empirical likelihood estimation of semiparametric models

研究成果: Article査読

10 被引用数 (Scopus)

抄録

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.

本文言語English
ページ(範囲)1923-1954
ページ数32
ジャーナルJournal of Multivariate Analysis
98
10
DOI
出版ステータスPublished - 2007 11
外部発表はい

ASJC Scopus subject areas

  • Statistics and Probability
  • Numerical Analysis
  • Statistics, Probability and Uncertainty

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