A new lack-of-fit test for quantile regression with censored data

被引：5

作者：

Conde-Amboage, Mercedes ^{[1
,2
]}

Van Keilegom, Ingrid ^{[2
]}

Gonzalez-Manteiga, Wenceslao ^{[1
]}

机构：

[1] Univ Santiago de Compostela, Dept Stat Math Anal & Optimizat, Models Optimizat Decis Stat & Applicat Res Grp MO, Santiago De Compostela, Spain

[2] Katholieke Univ Leuven, Res Ctr Operat Res & Stat ORSTAT, Leuven, Belgium

来源：

SCANDINAVIAN JOURNAL OF STATISTICS | 2021年 / 48卷 / 02期

基金：

欧盟地平线“2020”;

关键词：

bootstrap calibration; censored data; lack-of-fit tests; quantile regression;

D O I：

10.1111/sjos.12512

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

A new lack-of-fit test for quantile regression models will be presented for the case where the response variable is right-censored. The test is based on the cumulative sum of residuals, and it extends the ideas of He and Zhu (2003) to censored quantile regression. It will be shown that the empirical process associated with the test statistic converges to a Gaussian process under the null hypothesis and is consistent. To approximate the critical values of the test, a bootstrap mechanism will be used. A simulation study will be carried out to study the performance of the new test in comparison with other tests available in the literature. Finally, a real data application will be presented to show the good properties of the new lack-of-fit test in practice.

引用

页码：655 / 688

页数：34

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