Journal ArticleDOI
Bayesian inference for the generalized exponential distribution
Mohamed Z. Raqab,Mohamed T. Madi +1 more
TLDR
In this paper, the Bayesian estimation and prediction for the generalized exponential (GE) distribution, using informative priors, have been considered, and the Gibbs and Metropolis samplers data sets are used to predict the behavior of further observations from the distribution.Abstract:
The two-parameter generalized exponential (GE) distribution was introduced by Gupta and Kundu [Gupta, R.D. and Kundu, D., 1999, Generalized exponential distribution. Australian and New Zealand Journal of Statistics, 41(2), 173–188.]. It was observed that the GE can be used in situations where a skewed distribution for a nonnegative random variable is needed. In this article, the Bayesian estimation and prediction for the GE distribution, using informative priors, have been considered. Importance sampling is used to estimate the parameters, as well as the reliability function, and the Gibbs and Metropolis samplers data sets are used to predict the behavior of further observations from the distribution. Two data sets are used to illustrate the Bayesian procedure.read more
Citations
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Journal ArticleDOI
Generalized exponential distribution: Bayesian estimations
Debasis Kundu,Rameshwar D. Gupta +1 more
TL;DR: The approximate Bayes estimators obtained under the assumptions of non-informative priors, are compared with the maximum likelihood estimators using Monte Carlo simulations.
Journal ArticleDOI
Hybrid censoring
TL;DR: This review discusses Type-I and Type-II hybrid censoring schemes and associated inferential issues, and describes inferential methods based on them, and point out their advantages and disadvantages.
Journal ArticleDOI
A new exponential-type distribution with constant, decreasing, increasing, upside-down bathtub and bathtub-shaped failure rate function
TL;DR: A new three-parameter exponential-type family of distributions which can be used in modeling survival data, reliability problems and fatigue life studies is introduced and maximum likelihood estimation of the unknown parameters of the new model for complete sample as well as for censored sample is discussed.
Journal ArticleDOI
Reliability estimation in generalized inverted exponential distribution with progressively type II censored sample
Hare Krishna,Kapil Kumar +1 more
TL;DR: Abouammoh and Alshingiti as discussed by the authors considered a generalization of inverted exponential distribution as a lifetime model and derived the maximum likelihood estimation of the two parameters involved along with reliability and failure rate functions.
Journal ArticleDOI
Estimating the Parameters of the Generalized Exponential Distribution in Presence of Hybrid Censoring
Debasis Kundu,Biswabrata Pradhan +1 more
TL;DR: The analysis of hybrid censored data when the lifetime distribution of the individual item is a two-parameter generalized exponential distribution has been studied in this article, where the EM algorithm is used to compute the maximum likelihood estimators.
References
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BookDOI
Markov Chain Monte Carlo in Practice
TL;DR: The Markov Chain Monte Carlo Implementation Results Summary and Discussion MEDICAL MONITORING Introduction Modelling Medical Monitoring Computing Posterior Distributions Forecasting Model Criticism Illustrative Application Discussion MCMC for NONLINEAR HIERARCHICAL MODELS.
Book
Statistical Models and Methods for Lifetime Data
TL;DR: Inference procedures for Log-Location-Scale Distributions as discussed by the authors have been used for estimating likelihood and estimating function methods. But they have not yet been applied to the estimation of likelihood.
Journal ArticleDOI
Markov Chains for Exploring Posterior Distributions
TL;DR: Several Markov chain methods are available for sampling from a posterior distribution as discussed by the authors, including Gibbs sampler and Metropolis algorithm, and several strategies for constructing hybrid algorithms, which can be used to guide the construction of more efficient algorithms.
Journal ArticleDOI
Statistical Models and Methods for Lifetime Data
TL;DR: This book describes and illustrates how to compute a simple “naive” variance estimate and con dence intervals that would be correct under the assumption of an underlying nonhomogeneous Poisson process model.
Related Papers (5)
Generalized exponential distribution: Existing results and some recent developments
Rameshwar D. Gupta,Debasis Kundu +1 more