کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1150161 | 957915 | 2007 | 16 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Adaptive estimation of error density in nonparametric regression with small sample size
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موضوعات مرتبط
مهندسی و علوم پایه
ریاضیات
ریاضیات کاربردی
پیش نمایش صفحه اول مقاله

چکیده انگلیسی
It has been established recently in Efromovich [2005. Estimation of the density of regression errors. Ann. Statist. 33, 2194-2227] that, under a mild assumption, the error density in a nonparametric regression can be asymptotically estimated with the accuracy of an oracle that knows underlying regression errors. The asymptotic nature of the result, and in particular the used methodology of splitting data for estimating nuisance functions and the error density, does not make an asymptotic estimator, suggested in that article, feasible for practically interesting cases of small sample sizes. This article continues the research and solves two important issues. First, it shows that the asymptotic holds without splitting the data. Second, a data-driven estimator, based on the new asymptotic, is suggested and then tested on real and simulated examples.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Statistical Planning and Inference - Volume 137, Issue 2, 1 February 2007, Pages 363-378
Journal: Journal of Statistical Planning and Inference - Volume 137, Issue 2, 1 February 2007, Pages 363-378
نویسندگان
Sam Efromovich,