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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/9517

Title: Flexible modeling based on copulas in nonparametric median regression
Authors: BRAEKERS, Roel
VAN KEILEGOM, Ingrid
Issue Date: 2009
Publisher: Elsevier Inc.
Citation: JOURNAL OF MULTIVARIATE ANALYSIS, 100(6). p. 1270-1281
Abstract: Consider the model Y=m(X)+ε, where m()=med(Y|) is unknown but smooth. It is often assumed that ε and X are independent. However, in practice this assumption is violated in many cases. In this paper we propose modeling the dependence between ε and X by means of a copula model, i.e. , where is a copula function depending on an unknown parameter θ, and Fε and FX are the marginals of ε and X. Since many parametric copula families contain the independent copula as a special case, the so-obtained regression model is more flexible than the ‘classical’ regression model. We estimate the parameter θ via a pseudo-likelihood method and prove the asymptotic normality of the estimator, based on delicate empirical process theory. We also study the estimation of the conditional distribution of Y given X. The procedure is illustrated by means of a simulation study, and the method is applied to data on food expenditures in households.
URI: http://hdl.handle.net/1942/9517
DOI: 10.1016/j.jmva.2008.11.009
ISI #: 000265805600014
ISSN: 0047-259X
Category: A1
Type: Journal Contribution
Validation: ecoom, 2010
Appears in Collections: Research publications

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