PREDICTION VIA THE CONDITIONAL QUANTILE FOR RIGHT CENSORSHIP MODEL

Elias Ould-Saïd (France) and Ourida Sadki (Algérie)

Received September 20, 2007

Abstract

In this paper we introduce a new smooth estimator of the conditional quantile function in the censorship model. We show that this estimator converges uniformly almost surely and suitably normalized is asymptotically normal. Some simulations have been drawn to lend further support to our theoretical results for the convergence as well as for the normality for the finite samples.

Keywords and phrases: censored data, conditional distribution function, conditional quantile, Kaplan-Meier estimator, kernel estimator.