کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
9741803 1489781 2005 29 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Efficient semiparametric estimators via modified profile likelihood
موضوعات مرتبط
مهندسی و علوم پایه ریاضیات ریاضیات کاربردی
پیش نمایش صفحه اول مقاله
Efficient semiparametric estimators via modified profile likelihood
چکیده انگلیسی
A new strategy is developed for obtaining large-sample efficient estimators of finite-dimensional parameters β within semiparametric statistical models. The key idea is to maximize over β a nonparametric log-likelihood with the infinite-dimensional nuisance parameter λ replaced by a consistent preliminary estimator λ˜β of the Kullback-Leibler minimizing value λβ for fixed β. It is shown that the parametric submodel with Kullback-Leibler minimizer substituted for λ is generally a least-favorable model. Results extending those of Severini and Wong (Ann. Statist. 20 (1992) 1768) then establish efficiency of the estimator of β maximizing log-likelihood with λ replaced for fixed β by λ˜β. These theoretical results are specialized to censored linear regression and to a class of semiparametric survival analysis regression models including the proportional hazards models with unobserved random effect or `frailty', the latter through results of Slud and Vonta (Scand. J. Statist. 31 (2004) 21) characterizing the restricted Kullback-Leibler information minimizers.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Statistical Planning and Inference - Volume 129, Issues 1–2, 15 February 2005, Pages 339-367
نویسندگان
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