BAYESIAN ESTIMATION OF SCALE PARAMETER OF THE LOG-LOGISTIC DISTRIBUTION UNDER THE ASSUMPTION OF CHI-SQUARE AND MAXWELL PRIORS

Abubakar Yahaya, Mustapha Muhammad Dewu

Abstract


In this paper, the estimation of the scale parameter of log-logistic distribution was addressed under the assumption of chi-square and Maxwell priors wherein the Bayes estimates and posterior risks were derived under squared error loss function(SELF) and precautionary loss function(PLF). Furthermore, a simulation study was undertaken using the Markov-chain Monte Carlo(MCMC) simulation technique in order to assess the performance of the assumed prior distributions and loss functions. Among the two priors used in this study, chi-square prior appears to yield lower posterior risks thereby producing the best estimate and when compared with the estimates produced under the assumption of non-informative priors (uniform and Jeffrey’s), we realized that indeed the assumption of informative priors yields better estimates and overall the precautionary loss function performs better for the estimation of the parameter of interest.


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References


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