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October 2002 Density and hazard estimation in censored regression models
Ingrid Van Keilegom, Noël Veraverbeke
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Bernoulli 8(5): 607-625 (October 2002).


Let (X,Y) be a random vector, where Y denotes the variable of interest, possibly subject to random right censoring, and X is a covariate. Consider a heteroscedastic model Y=m(X)+σ(X)ε, where the error term ε is independent of X and m(X) and σ(X) are smooth but unknown functions. Under this model, we construct a nonparametric estimator for the density and hazard function of Y given X, which has a faster rate of convergence than the completely nonparametric estimator that is constructed without making any model assumption. Moreover, the proposed estimator for the density and hazard function performs better than the classical nonparametric estimator, especially in the right tail of the distribution.


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Ingrid Van Keilegom. Noël Veraverbeke. "Density and hazard estimation in censored regression models." Bernoulli 8 (5) 607 - 625, October 2002.


Published: October 2002
First available in Project Euclid: 4 March 2004

zbMATH: 1007.62029
MathSciNet: MR2003G:62077

Keywords: Asymptotic representation , density function , hazard rate , heteroscedastic regression , right censoring , weak convergence

Rights: Copyright © 2002 Bernoulli Society for Mathematical Statistics and Probability

Vol.8 • No. 5 • October 2002
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