Robust Bayesian Analysis of Generalized Half Logistic Distribution

  • Ajit Chaturvedi University of Delhi
  • Taruna Kumari University of Delhi
Keywords: GHLD, Ɛ-contamination class of priors, ML-II posterior density, Hazard-rate, Reliability function, SELF and LINEX losses, type-II censoring, sampling scheme by Bartholomew, Markov Chain Monte Carlo (MCMC) procedure, Metropolis-Hastings algorithm.


In this paper, Robust Bayesian analysis of the generalized half logistic distribution (GHLD) under an $\epsilon$-contamination class of priors for the shape parameter $\lambda$ is considered. ML-II Bayes estimators of the parameters, reliability function and hazard function are derived under the squared-error loss function (SELF) and linear exponential (LINEX) loss function by considering the Type~II censoring and the sampling scheme of Bartholomew (1963). Both the cases when scale parameter is known and unknown is considered under Type~II censoring and under the sampling scheme of Bartholomew. Simulation study and analysis of a real data set are presented.

Author Biographies

Ajit Chaturvedi, University of Delhi
Statistics, Assosiate Professor
Taruna Kumari, University of Delhi
Statistics, Assistant Professor


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How to Cite
Chaturvedi, A., & Kumari, T. (2017). Robust Bayesian Analysis of Generalized Half Logistic Distribution. Statistics, Optimization & Information Computing, 5(2), 158-178.
Research Articles