Bayesian Estimation for the Two Log-Logistic Models Under Joint Type II Censoring Schemes

  • Ranjita Pandey Department of Statistics, University of Delhi, Delhi, India
  • Pulkit Srivastava Department of Statistics, University of Delhi, Delhi, India
Keywords: Log-logistic model, Bayes estimation, Joint type II censoring scheme, Bayesian credible interval, Markov Chain Monte Carlo

Abstract

The present paper, discusses classical and Bayesian estimation of unknown combined parameters of two different log-logistic models with common shape parameters and different scale parameters under a new type of censoring scheme known as joint type II censoring scheme. Maximum likelihood estimators are derived. Bayes estimates of parameters are proposed under different loss functions. Classical asymptotic confidence intervals along with the Bayesian credible intervals and Highest Posterior Density region are also constructed. Markov Chain Monte Carlo approximation method is used for simulating the theoretic results. Comparative assessment of the classical and the Bayes results are illustrated through a real archived dataset.

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Author Biographies

Ranjita Pandey, Department of Statistics, University of Delhi, Delhi, India

Ranjita Pandey is an esteemed faculty at Department of Statistics, University of Delhi, Delhi. She has more than 20 years of experience of teaching and research. Her research areas include theoretical and applied Bayesian Inference, lifetime distributions, time series models, demography, imputation methods and ecological modelling. She has extensive administrative experience. She has delivered many invited lectures and has served as reviewer for several journals.

Pulkit Srivastava, Department of Statistics, University of Delhi, Delhi, India

Pulkit Srivastava is currently pursuing his Ph.D. at the Department of Statistics, University of Delhi, Delhi. His research areas include Bayesian Inference, Stochastic processes etc.

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Published
2022-04-20
Section
Articles