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dc.contributor.authorEl Ghouch, Anouar
dc.contributor.authorGenton, Marc G.
dc.date.accessioned2021-09-13T12:59:36Z
dc.date.available2021-09-13T12:59:36Z
dc.date.issued2009
dc.identifier.citationEl Ghouch, A., & Genton, M. G. (2009). Local Polynomial Quantile Regression With Parametric Features. Journal of the American Statistical Association, 104(488), 1416–1429. doi:10.1198/jasa.2009.tm08400
dc.identifier.issn1537-274X
dc.identifier.issn0162-1459
dc.identifier.doi10.1198/jasa.2009.tm08400
dc.identifier.urihttp://hdl.handle.net/10754/671196
dc.description.abstractWe propose a new approach to conditional quantile function estimation that combines both parametric and nonparametric techniques. At each design point, a global, possibly incorrect, pilot parametric model is locally adjusted through a kernel smoothing fit. The resulting quantile regression estimator behaves like a parametric estimator when the latter is correct and converges to the nonparametric solution as the parametric start deviates from the true underlying model. We give a Bahadur-type representation of the proposed estimator from which consistency and asymptotic normality are derived under an α-mixing assumption. We also propose a practical bandwidth selector based on the plug-in principle and discuss the numerical implementation of the new estimator. Finally, we investigate the performance of the proposed method via simulations and illustrate the methodology with a data example. © 2009 American Statistical Association.
dc.description.sponsorshipFinancial support from the Swiss National Science Foundation (project 116019) is gratefully acknowledged. Genton’s research was supported in part by National Science Foundation grants DMS-0504896 and CMG ATM-0620624 and by King Abdullah University of Science and Technology award KUS-C1-016-04. The authors thank the editor, an associate editor, and two anonymous referees for their valuable comments.
dc.publisherInforma UK Limited
dc.relation.urlhttp://www.tandfonline.com/doi/abs/10.1198/jasa.2009.tm08400
dc.rightsArchived with thanks to JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
dc.subjectBias reduction
dc.subjectLocal polynomial smoothing
dc.subjectModel misspecification
dc.subjectRobustness
dc.subjectStrong mixing sequence
dc.titleLocal Polynomial Quantile Regression With Parametric Features
dc.typeArticle
dc.identifier.journalJOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
dc.rights.embargodate2022-09-13
dc.identifier.wosutWOS:000273995500012
dc.eprint.versionPost-print
dc.contributor.institutionDepartment of Econometrics, University of Geneva, CH-1211 Geneva 4, Switzerland
dc.contributor.institutionDepartment of Statistics, Texas A and M University, College Station, TX 77843-3143, United States
dc.identifier.volume104
dc.identifier.issue488
dc.identifier.pages1416-1429
dc.identifier.eid2-s2.0-74049113273


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