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Article: A lack-of-fit test for quantile regression

TitleA lack-of-fit test for quantile regression
Authors
KeywordsConsistency
Cusum process
Empirical process
Goodness-of-fit
Linear regression
Issue Date2003
PublisherAmerican Statistical Association. The Journal's web site is located at http://www.amstat.org/publications/jasa/index.cfm?fuseaction=main
Citation
Journal of the American Statistical Association, 2003, v. 98 n. 464, p. 1013-1022 How to Cite?
AbstractWe propose a new lack-of-fit test for quantile regression models that is suitable even with high-dimensional covariates. The test is based on the cumulative sum of residuals with respect to unidimensional linear projections of the covariates. The test adapts concepts proposed by Escanciano (Econometric Theory, 22, 2006) to cope with many covariates to the test proposed by He and Zhu (Journal of the American Statistical Association, 98, 2003). To approximate the critical values of the test, a wild bootstrap mechanism is used, similar to that proposed by Feng et al. (Biometrika, 98, 2011). 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.
Persistent Identifierhttp://hdl.handle.net/10722/224198
ISSN
2015 Impact Factor: 1.725
2015 SCImago Journal Rankings: 3.447

 

DC FieldValueLanguage
dc.contributor.authorHe, X-
dc.contributor.authorZhu, LX-
dc.date.accessioned2016-03-29T07:11:51Z-
dc.date.available2016-03-29T07:11:51Z-
dc.date.issued2003-
dc.identifier.citationJournal of the American Statistical Association, 2003, v. 98 n. 464, p. 1013-1022-
dc.identifier.issn0162-1459-
dc.identifier.urihttp://hdl.handle.net/10722/224198-
dc.description.abstractWe propose a new lack-of-fit test for quantile regression models that is suitable even with high-dimensional covariates. The test is based on the cumulative sum of residuals with respect to unidimensional linear projections of the covariates. The test adapts concepts proposed by Escanciano (Econometric Theory, 22, 2006) to cope with many covariates to the test proposed by He and Zhu (Journal of the American Statistical Association, 98, 2003). To approximate the critical values of the test, a wild bootstrap mechanism is used, similar to that proposed by Feng et al. (Biometrika, 98, 2011). 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.-
dc.languageeng-
dc.publisherAmerican Statistical Association. The Journal's web site is located at http://www.amstat.org/publications/jasa/index.cfm?fuseaction=main-
dc.relation.ispartofJournal of the American Statistical Association-
dc.subjectConsistency-
dc.subjectCusum process-
dc.subjectEmpirical process-
dc.subjectGoodness-of-fit-
dc.subjectLinear regression-
dc.titleA lack-of-fit test for quantile regression-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1198/016214503000000963-
dc.identifier.hkuros95420-
dc.identifier.volume98-
dc.identifier.issue464-
dc.identifier.spage1013-
dc.identifier.epage1022-
dc.publisher.placeUnited States-

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