A new lifetime model with variable shapes for the hazard rate
Brazilian Journal of Probability and Statistics • 2017
Publication Information
Authors
Ahmed Z. Afify;Gauss M. Cordeiro;Nadeem Shafique Butt;Edwin M. M. Ortega;Adriano K. Suzuki
Keywords
Censored data;complementary Weibull geometric;generating function;
maximum likelihood;order statistic
Journal
Brazilian Journal of Probability and Statistics
Publisher
Brazilian Statistical Association
Volume
31
Issue
3
Pages
516-541
publication.type
International
Paper Link
Open Link
Supplementary Materials
Not Available
Abstract
We define and study a new generalization of the complementary
Weibull geometric distribution introduced by Tojeiro et al. (J. Stat. Comput. Simul.84(2014) 1345–1362). The new lifetime model is referred to as the Kumaraswamy complementary Weibull geometric distribution and includes twenty three special models. Its hazard rate function can be constant, increasing, decreasing, bathtub and unimodal shaped. Some of its mathematical
properties, including explicit expressions for the ordinary and incomplete moments, generating and quantile functions, Rényi entropy, mean residual life and mean inactivity time are derived. The method of maximum likelihood is used for estimating the model parameters. We provide some simulation results to assess the performance of the proposed model. Two applications
to real data sets show the flexibility of the new model compared with some nested and non-nested models.
Weibull geometric distribution introduced by Tojeiro et al. (J. Stat. Comput. Simul.84(2014) 1345–1362). The new lifetime model is referred to as the Kumaraswamy complementary Weibull geometric distribution and includes twenty three special models. Its hazard rate function can be constant, increasing, decreasing, bathtub and unimodal shaped. Some of its mathematical
properties, including explicit expressions for the ordinary and incomplete moments, generating and quantile functions, Rényi entropy, mean residual life and mean inactivity time are derived. The method of maximum likelihood is used for estimating the model parameters. We provide some simulation results to assess the performance of the proposed model. Two applications
to real data sets show the flexibility of the new model compared with some nested and non-nested models.
Staff Members - Benha University