Lack-of-fit tests for quantile regression models

Lack-of-fit tests for quantile regression models

The paper novelly transforms lack-of-fit tests for parametric quantile regression models into checking the equality of two conditional distributions of covariates. Accordingly, by applying some successful two-sample test statistics in the literature, two tests are constructed to check the lack of fi...

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Veröffentlicht in:Journal of the Royal Statistical Society. Series B (Statistical Methodology). - Blackwell Publishers. - 81(2019), 3, Seite 629-648
1. Verfasser: Dong, Chen (VerfasserIn)
Weitere Verfasser: Li, Guodong, Feng, Xingdong
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2019
Zugriff auf das übergeordnete Werk:Journal of the Royal Statistical Society. Series B (Statistical Methodology)
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520 |a The paper novelly transforms lack-of-fit tests for parametric quantile regression models into checking the equality of two conditional distributions of covariates. Accordingly, by applying some successful two-sample test statistics in the literature, two tests are constructed to check the lack of fit for low and high dimensional quantile regression models. The low dimensional test works well when the number of covariates is moderate, whereas the high dimensional test can maintain the power when the number of covariates exceeds the sample size. The null distribution of the high dimensional test has an explicit form, and the p-values or critical values can then be calculated directly. The finite sample performance of the tests proposed is examined by simulation studies, and their usefulness is further illustrated by two real examples. 
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700 1 |a Feng, Xingdong  |e verfasserin  |4 aut 
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