Analysis of Incomplete, Censored Data in Competing Risks Models With Generalized Exponential Distributions

Abstract

This paper presents estimates of the parameters included in a competing risks model in the presence of incomplete & censored data. We consider the case when the competing risks have generalized exponential distributions. The maximum likelihood procedure is used to derive point, and asymptotic confidence interval estimations of the unknown parameters. The relative risks due to each cause of failure are investigated. A set of real data is used to test the hypothesis that the causes of failure follow exponential distributions against that they follow generalized exponential distributions