کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
5129350 1489645 2017 18 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Profile forward regression screening for ultra-high dimensional semiparametric varying coefficient partially linear models
موضوعات مرتبط
مهندسی و علوم پایه ریاضیات آنالیز عددی
پیش نمایش صفحه اول مقاله
Profile forward regression screening for ultra-high dimensional semiparametric varying coefficient partially linear models
چکیده انگلیسی

In this paper, we consider semiparametric varying coefficient partially linear models when the predictor variables of the linear part are ultra-high dimensional where the dimensionality grows exponentially with the sample size. We propose a profile forward regression (PFR) method to perform variable screening for ultra-high dimensional linear predictor variables. The proposed PFR algorithm can not only identify all relevant predictors consistently even for ultra-high semiparametric models including both nonparametric and parametric parts, but also possesses the screening consistency property. To determine whether or not to include the candidate predictor in the model of selected ones, we adopt an extended Bayesian information criterion (EBIC) to select the “best” candidate model. Simulation studies and a real data example are also carried out to assess the performance of the proposed method and to compare it with existing screening methods.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Multivariate Analysis - Volume 155, March 2017, Pages 133-150
نویسندگان
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