Inferences for Gompertz Model:Bayesian and non-Bayesian Approaches
Bayesian and non-Bayesian Approaches
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The Gompertz distribution plays an important role in modeling survival times, human mortality, growth model and actuarial tables. The subject of progressive censoring has received considerable attention in the past few years, due in part to the availability of high speed computing resources, which make it both a feasible topic for simulation studies for researchers and a feasible method of gathering lifetime data for practitioners. In this book, we have considered Bayesian and non Bayesian estimators for Gompertz parameters, some survival time parameters, namely, reliability and hazard functio...
The Gompertz distribution plays an important role in modeling survival times, human mortality, growth model and actuarial tables. The subject of progressive censoring has received considerable attention in the past few years, due in part to the availability of high speed computing resources, which make it both a feasible topic for simulation studies for researchers and a feasible method of gathering lifetime data for practitioners. In this book, we have considered Bayesian and non Bayesian estimators for Gompertz parameters, some survival time parameters, namely, reliability and hazard functions and the coefficient of variation by using both progressive first-failure censoring scheme and an adaptive Type-II progressive censoring scheme. We have considered Bayesian and non Bayesian approaches Also, we develop different confidence intervals, using asymptotic distributions of the maximum likelihood estimators and two different bootstrap methods. Also, we shown how record data can beused to provide inferences for the stress strength reliability model using Markov chain Monte Carlo (MCMC). Bayesian prediction intervals based on progressive first-failure-censored have been discussed
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