A new long-term lifetime distribution induced by a latent complementary risk framework

Abstract

In this paper, we proposed a new three-parameter long-term lifetime distribution induced by a latent complementary risk framework with decreasing, increasing and unimodal hazard function, the long-term complementary exponential geometric distribution. The new distribution arises from latent competing risk scenarios, where the lifetime associated scenario, with a particular risk, is not observable, rather we observe only the maximum lifetime value among all risks, and the presence of long-term survival. The properties of the proposed distribution are discussed, including its probability density function and explicit algebraic formulas for its reliability, hazard and quantile functions and order statistics. The parameter estimation is based on the usual maximum-likelihood approach. A simulation study assesses the performance of the estimation procedure. We compare the new distribution with its particular cases, as well as with the long-term Weibull distribution on three real data sets, observing its potential and competitiveness in comparison with some usual long-term lifetime distributions.

Keywords:

• complementary exponential geometric distribution
• latent complementary risks
• long-term survivals
• ovarian cancer data
• glioma data
• credit scoring

Acknowledgements

The authors thank the Editorial Boarding and the anonymous referees for their comments and suggestions. The research is supported by the Brazilian organizations CAPES and CNPq.

Notes

http://www.cancer.org/cancer/ovariancancer/overviewguide/ovarian-cancer-overview-survival-rates