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 |
Issue Date: | 2002 | Publisher: | INT STATISTICAL INST | Source: | 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. | Document URI: | http://hdl.handle.net/1942/204 | ISSN: | 1350-7265 | e-ISSN: | 1573-9759 | ISI #: | 000179006700003 | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2003 |
Appears in Collections: | Research publications |
Show full item record
WEB OF SCIENCETM
Citations
16
checked on Oct 7, 2024
Page view(s)
20
checked on Jul 18, 2023
Google ScholarTM
Check
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.