کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6869609 | 681506 | 2015 | 11 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A lack-of-fit test for quantile regression models with high-dimensional covariates
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نظریه محاسباتی و ریاضیات
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چکیده انگلیسی
A new lack-of-fit test for quantile regression models, that is suitable even with high-dimensional covariates, is proposed. The test is based on the cumulative sum of residuals with respect to unidimensional linear projections of the covariates. To approximate the critical values of the test, a wild bootstrap mechanism convenient for quantile regression is used. An extensive simulation study was undertaken that shows the good performance of the new test, particularly when the dimension of the covariate is high. The test can also be applied and performs well under heteroscedastic regression models. The test is illustrated with real data about the economic growth of 161 countries.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computational Statistics & Data Analysis - Volume 88, August 2015, Pages 128-138
Journal: Computational Statistics & Data Analysis - Volume 88, August 2015, Pages 128-138
نویسندگان
Mercedes Conde-Amboage, César Sánchez-Sellero, Wenceslao González-Manteiga,