کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
10139312 1645952 2019 13 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Quantile regression for functional partially linear model in ultra-high dimensions
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نظریه محاسباتی و ریاضیات
پیش نمایش صفحه اول مقاله
Quantile regression for functional partially linear model in ultra-high dimensions
چکیده انگلیسی
Quantile regression for functional partially linear model in ultra-high dimensions is proposed and studied. By focusing on the conditional quantiles, where conditioning is on both multiple random processes and high-dimensional scalar covariates, the proposed model can lead to a comprehensive description of the scalar response. To select and estimate important variables, a double penalized functional quantile objective function with two nonconvex penalties is developed, and the optimal tuning parameters involved can be chosen by a two-step technique. Based on the difference convex analysis (DCA), the asymptotic properties of the resulting estimators are established, and the convergence rate of the prediction of the conditional quantile function can be obtained. Simulation studies demonstrate a competitive performance against the existing approach. A real application to Alzheimer's Disease Neuroimaging Initiative (ADNI) data is used to illustrate the practicality of the proposed model.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computational Statistics & Data Analysis - Volume 129, January 2019, Pages 135-147
نویسندگان
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