Bootstrapping quantiles in a fixed design regression model with censored data
(1998) Journal of Statistical Planning and Inference — Vol. 69, n° 1, p. 115-131 (1998)
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- Authors
- Abstract
- 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.
- Affiliations
Limburgs Universitair Centrum
Citations
Van Keilegom, I., & Veraverbeke, N. (1998). Bootstrapping quantiles in a fixed design regression model with censored data. Journal of Statistical Planning and Inference, 69(1), 115-131. https://doi.org/10.1016/S0378-3758(97)00126-2 (Original work published 1998)