The copula-graphic estimator in censored nonparametric location-scale regression models

Aleksandar Sujica and Ingrid Van Keilegom ()

Econometrics and Statistics, 2018, vol. 7, issue C, 89-114

Abstract: A common assumption when working with randomly right censored data, is the independence between the variable of interest Y (the survival time) and the censoring variable C. This assumption, which is not testable, is however unrealistic in certain situations. Let us assume that for a given covariate X, the dependence between the variables Y and C is described via a known copula. Additionally assume that Y is the response variable of a heteroscedastic regression model Y=m(X)+σ(X)ɛ, where the error term ε is independent of the explanatory variable X, and the functions m and σ are ‘smooth’. An estimator of the conditional distribution of Y given X under this model is then proposed, and the asymptotic normality of this estimator is shown. The small sample performance of the estimator is also studied, and the advantages/drawbacks of this estimator with respect to competing estimators are discussed.

Keywords: Asymptotic normality; Asymptotic representation; Copula; Dependent censoring; Kernel estimator; Nonparametric regression; Right censoring (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecosta:v:7:y:2018:i:c:p:89-114

DOI: 10.1016/j.ecosta.2017.07.002

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