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Article: Lack-of-fit Tests For Quantile Regression Models

TitleLack-of-fit Tests For Quantile Regression Models
Authors
KeywordsHigh dimensional data
Hypothesis test
Lack of fit
Quantile regression
Two-sample test
Issue Date2019
PublisherWiley-Blackwell Publishing Ltd. The Journal's web site is located at http://www.blackwellpublishing.com/journals/RSSB
Citation
Journal of the Royal Statistical Society. Series B: Statistical Methodology, 2019, v. 81 n. 3, p. 629-648 How to Cite?
AbstractThe 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.
Persistent Identifierhttp://hdl.handle.net/10722/272964
ISSN
2020 Impact Factor: 4.488
2015 SCImago Journal Rankings: 7.429
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorDong, C-
dc.contributor.authorLi, G-
dc.contributor.authorFeng, X-
dc.date.accessioned2019-08-06T09:20:00Z-
dc.date.available2019-08-06T09:20:00Z-
dc.date.issued2019-
dc.identifier.citationJournal of the Royal Statistical Society. Series B: Statistical Methodology, 2019, v. 81 n. 3, p. 629-648-
dc.identifier.issn1369-7412-
dc.identifier.urihttp://hdl.handle.net/10722/272964-
dc.description.abstractThe 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.-
dc.languageeng-
dc.publisherWiley-Blackwell Publishing Ltd. The Journal's web site is located at http://www.blackwellpublishing.com/journals/RSSB-
dc.relation.ispartofJournal of the Royal Statistical Society. Series B: Statistical Methodology-
dc.rightsThis is the peer reviewed version of the following article: Journal of the Royal Statistical Society. Series B: Statistical Methodology, 2019, v. 81 n. 3, p. 629-648, which has been published in final form at https://doi.org/10.1111/rssb.12321. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.-
dc.subjectHigh dimensional data-
dc.subjectHypothesis test-
dc.subjectLack of fit-
dc.subjectQuantile regression-
dc.subjectTwo-sample test-
dc.titleLack-of-fit Tests For Quantile Regression Models-
dc.typeArticle-
dc.identifier.emailLi, G: gdli@hku.hk-
dc.identifier.authorityLi, G=rp00738-
dc.description.naturepostprint-
dc.identifier.doi10.1111/rssb.12321-
dc.identifier.scopuseid_2-s2.0-85065185568-
dc.identifier.hkuros299669-
dc.identifier.volume81-
dc.identifier.issue3-
dc.identifier.spage629-
dc.identifier.epage648-
dc.identifier.isiWOS:000470714200007-
dc.publisher.placeUnited Kingdom-
dc.identifier.issnl1369-7412-

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