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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/204

Title: Density and hazard estimation in censored regression models
Authors: VAN KEILEGOM, Ingrid
VERAVERBEKE, Noel
Keywords: Mathematical Statistics
Non and semiparametric methods
Issue Date: 2002
Publisher: INT STATISTICAL INST
Citation: Bernouilli, 8(5). p. 607-625
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.
URI: http://hdl.handle.net/1942/204
ISI #: 000179006700003
ISSN: 1350-7265
Category: A1
Type: Journal Contribution
Validation: ecoom, 2003
Appears in Collections: Research publications

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