Bernoulli

  • Bernoulli
  • Volume 8, Number 5 (2002), 607-625.

Density and hazard estimation in censored regression models

Ingrid Van Keilegom and Noël Veraverbeke

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Abstract

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.

Article information

Source
Bernoulli, Volume 8, Number 5 (2002), 607-625.

Dates
First available in Project Euclid: 4 March 2004

Permanent link to this document
https://projecteuclid.org/euclid.bj/1078435220

Mathematical Reviews number (MathSciNet)
MR2003g:62077

Zentralblatt MATH identifier
1007.62029

Keywords
asymptotic representation density function hazard rate heteroscedastic regression right censoring weak convergence

Citation

Van Keilegom, Ingrid; Veraverbeke, Noël. Density and hazard estimation in censored regression models. Bernoulli 8 (2002), no. 5, 607--625. https://projecteuclid.org/euclid.bj/1078435220


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