کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
415283 681196 2016 17 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Robust shrinkage estimation and selection for functional multiple linear model through LAD loss
ترجمه فارسی عنوان
برآورد انقباض قوی و انتخاب برای مدل خطی چندگانه کاربردی از طریق از دست دادن LAD
کلمات کلیدی
مدل خطی کاربردی؛ انتخاب متغیرها؛ نیرومندی؛ پیش بینی های چند عملکردی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نظریه محاسباتی و ریاضیات
چکیده انگلیسی

In functional data analysis (FDA), variable selection in regression model is an important issue when there are multiple functional predictors. Most of the existing methods are based on least square loss and consequently sensitive to outliers in error. Robust variable selection procedure is desirable. When functional predictors are considered, both non-data-driven basis (e.g. B-spline) and data-driven basis (e.g. functional principal component (FPC)) are commonly used. The data-driven basis is flexible and adaptive, but it raise some difficulties, since the basis must be estimated from data.Since least absolute deviation (LAD) loss has been proven robust to the outliers in error, we propose in this paper a robust variable selection with data-driven basis FPC and LAD loss function. The asymptotic results are established for both fixed and diverging pp. Our results include the existing results as special cases. Simulation results and a real data example confirm the effectiveness of the proposed method.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computational Statistics & Data Analysis - Volume 103, November 2016, Pages 384–400
نویسندگان
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