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2017 Semiparametric copula quantile regression for complete or censored data
Mickaël De Backer, Anouar El Ghouch, Ingrid Van Keilegom
Electron. J. Statist. 11(1): 1660-1698 (2017). DOI: 10.1214/17-EJS1273


When facing multivariate covariates, general semiparametric regression techniques come at hand to propose flexible models that are unexposed to the curse of dimensionality. In this work a semiparametric copula-based estimator for conditional quantiles is investigated for both complete or right-censored data. In spirit, the methodology is extending the recent work of Noh, El Ghouch and Bouezmarni [34] and Noh, El Ghouch and Van Keilegom [35], as the main idea consists in appropriately defining the quantile regression in terms of a multivariate copula and marginal distributions. Prior estimation of the latter and simple plug-in lead to an easily implementable estimator expressed, for both contexts with or without censoring, as a weighted quantile of the observed response variable. In addition, and contrary to the initial suggestion in the literature, a semiparametric estimation scheme for the multivariate copula density is studied, motivated by the possible shortcomings of a purely parametric approach and driven by the regression context. The resulting quantile regression estimator has the valuable property of being automatically monotonic across quantile levels. Additionally, the copula-based approach allows the analyst to spontaneously take account of common regression concerns such as interactions between covariates or possible transformations of the latter. From a theoretical prospect, asymptotic normality for both complete and censored data is obtained under classical regularity conditions. Finally, numerical examples as well as a real data application are used to illustrate the validity and finite sample performance of the proposed procedure.


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Mickaël De Backer. Anouar El Ghouch. Ingrid Van Keilegom. "Semiparametric copula quantile regression for complete or censored data." Electron. J. Statist. 11 (1) 1660 - 1698, 2017.


Received: 1 April 2016; Published: 2017
First available in Project Euclid: 25 April 2017

zbMATH: 06715787
MathSciNet: MR3639560
Digital Object Identifier: 10.1214/17-EJS1273

Keywords: censored quantile regression , kernel smoothing , multidimensional copula modelling , polynomial local-likelihood , probit transformation , semiparametric regression , semiparametric vine copulas


Vol.11 • No. 1 • 2017
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