Embargo End Date2022-09-13
Permanent link to this recordhttp://hdl.handle.net/10754/671196
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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.
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
SponsorsFinancial 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.
PublisherInforma UK Limited