Location estimation in nonparametric regression with censored data

Cédric Heuchenne and Ingrid Van Keilegom ()

Journal of Multivariate Analysis, 2007, vol. 98, issue 8, 1558-1582

Abstract: Consider the heteroscedastic model Y=m(X)+[sigma](X)[var epsilon], where [var epsilon] and X are independent, Y is subject to right censoring, m(·) is an unknown but smooth location function (like e.g. conditional mean, median, trimmed mean...) and [sigma](·) an unknown but smooth scale function. In this paper we consider the estimation of m(·) under this model. The estimator we propose is a Nadaraya-Watson type estimator, for which the censored observations are replaced by 'synthetic' data points estimated under the above model. The estimator offers an alternative for the completely nonparametric estimator of m(·), which cannot be estimated consistently in a completely nonparametric way, whenever high quantiles of the conditional distribution of Y given X=x are involved. We obtain the asymptotic properties of the proposed estimator of m(x) and study its finite sample behaviour in a simulation study. The method is also applied to a study of quasars in astronomy.

Keywords: Bandwidth; Censored; regression; Kernel; estimation; Location-scale; model; Nonparametric; regression; Survival; analysis (search for similar items in EconPapers)
Date: 2007
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