کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
415384 681202 2008 21 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Robust bandwidth selection in semiparametric partly linear regression models: Monte Carlo study and influential analysis
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نظریه محاسباتی و ریاضیات
پیش نمایش صفحه اول مقاله
Robust bandwidth selection in semiparametric partly linear regression models: Monte Carlo study and influential analysis
چکیده انگلیسی

In this paper, under a semiparametric partly linear regression model with fixed design, we introduce a family of robust procedures to select the bandwidth parameter. The robust plug-in proposal is based on nonparametric robust estimates of the ννth derivatives and under mild conditions, it converges to the optimal bandwidth. A robust cross-validation bandwidth is also considered and the performance of the different proposals is compared through a Monte Carlo study. We define an empirical influence measure for data-driven bandwidth selectors and, through it, we study the sensitivity of the data-driven bandwidth selectors. It appears that the robust selector compares favorably to its classical competitor, despite the need to select a pilot bandwidth when considering plug-in bandwidths. Moreover, the plug-in procedure seems to be less sensitive than the cross-validation in particular, when introducing several outliers. When combined with the three-step procedure proposed by Bianco and Boente [2004. Robust estimators in semiparametric partly linear regression models. J. Statist. Plann. Inference 122, 229–252] the robust selectors lead to robust data-driven estimates of both the regression function and the regression parameter.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computational Statistics & Data Analysis - Volume 52, Issue 5, 20 January 2008, Pages 2808–2828
نویسندگان
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