کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
1144621 957424 2014 14 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Component selection in additive quantile regression models
ترجمه فارسی عنوان
انتخاب کامپوننت در مدل های رگرسیون چندمتغیره افزودنی
موضوعات مرتبط
مهندسی و علوم پایه ریاضیات آمار و احتمال
چکیده انگلیسی

Nonparametric additive models are powerful techniques for multivariate data analysis. Although many procedures have been developed for estimating additive components both in mean regression and quantile regression, the problem of selecting relevant components has not been addressed much especially in quantile regression. We present a doubly-penalized estimation procedure for component selection in additive quantile regression models that combines basis function approximation with a ridge-type penalty and a variant of the smoothly clipped absolute deviation penalty. We show that the proposed estimator identifies relevant and irrelevant components consistently and achieves the nonparametric optimal rate of convergence for the relevant components. We also provide an accurate and efficient computation algorithm to implement the estimator and demonstrate its performance through simulation studies. Finally, we illustrate our method via a real data example to identify important body measurements to predict percentage of body fat of an individual.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of the Korean Statistical Society - Volume 43, Issue 3, September 2014, Pages 439–452
نویسندگان
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