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Bootstrapping quantiles in a fixed design regression model with censored data

✍ Scribed by Ingrid Van Keilegom; Noël Veraverbeke

Publisher
Elsevier Science
Year
1998
Tongue
English
Weight
766 KB
Volume
69
Category
Article
ISSN
0378-3758

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✦ Synopsis

We consider the problem of estimating the quantiles of a distribution function in a fixed design regression model in which the observations are subject to random right censoring. The quantile estimator is defined via a conditional Kaplan-Meier type estimator for the distribution at a given design point. We establish an a.s. asymptotic representation for this quantile estimator, from which we obtain its asymptotic normality. Because a complicated estimation procedure is necessary for estimating the asymptotic bias and variance, we use a resampling procedure, which provides us, via an asymptotic representation for the bootstrapped estimator, with an alternative for the normal approximation.

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