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
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
ریاضیات
ریاضیات کاربردی
پیش نمایش صفحه اول مقاله
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 λËβ 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 λËβ. 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 1â2, 15 February 2005, Pages 339-367
Journal: Journal of Statistical Planning and Inference - Volume 129, Issues 1â2, 15 February 2005, Pages 339-367
نویسندگان
Eric V. Slud, Filia Vonta,