Classical and Bayesian estimation in log-logistic distribution under random censoring

Kapil Kumar ()
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Kapil Kumar: Central University of Haryana

International Journal of System Assurance Engineering and Management, 2018, vol. 9, issue 2, No 10, 440-451

Abstract: Abstract This article deals with the classical and Bayesian estimation of the parameters of log-logistic distribution using random censorship model. The maximum likelihood estimators and the asymptotic confidence intervals based on observed Fisher information matrix of the parameters are derived. Bayes estimators of the parameters under generalized entropy loss function using independent gamma priors are obtained. For Bayesian computation, Tierney–Kadane’s approximation and Markov chain Monte Carlo (MCMC) methods are used. Also, the highest posterior credible intervals of the parameters based on MCMC method are constructed. A Monte Carlo simulation study is carried out to compare the behavior of various estimators developed in this article. Finally, a real data analysis is performed for illustration purposes.

Keywords: Random censoring; Log-logistic distribution; Maximum likelihood estimation; Asymptotic confidence intervals; Bayes estimation; MCMC method; HPD credible intervals (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:spr:ijsaem:v:9:y:2018:i:2:d:10.1007_s13198-017-0688-3

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DOI: 10.1007/s13198-017-0688-3

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