On Estimating The Quantile Function For Distributions Constrained by Star-Shaped Ordering

Abstract

Consider distributions F and G such that G -1 F is star-shaped. In the problem of estimating the quantile functions for lifetime distributions, the estimators developed by Rojo (1998) are compared with the commonly used empirical quantile function. Both the one-sample and the two-sample methods of estimation are considered for a wide class of lifetime distributions. In addition, the behavior of the estimators is examined for star-shaped ordered lifetime distributions of the important class of coherent k- out-of-n reliability systems. Results of a Monte Carlo study are presented which compare the behavior of the new estimators with that of the empirical quantile function interms of bias and mean-squared error. As the behavior of these estimators typically depends on the tail behavior of the underlying distributions, the examples presented here include distributions with short, medium and long tails. A formula for the inverse of the Kaplan-Meier estimator is provided and used to generate the simulations in the case of censored data.