Posterior and predictive inferences for Marshall Olkin bivariate Weibull distribution via Markov chain Monte Carlo methods

Rakesh Ranjan () and Vastoshpati Shastri
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Rakesh Ranjan: Banaras Hindu University
Vastoshpati Shastri: Government Arts & Science College

International Journal of System Assurance Engineering and Management, 2019, vol. 10, issue 6, No 11, 1535-1543

Abstract: Abstract This paper deals with the well known bi-variate Weibull distribution developed by Marshall and Olkin. In the light of prior information, this paper derives the posterior distribution and performs Markov chain Monte Carlo methods to obtain posterior based inferences. This paper also checks the sensitivity of posterior estimates by changing the prior variances followed by Bayesian prediction using sample-based approaches. Numerical illustrations are provided for real as well as simulated data sets.

Keywords: Bivariate model; ML Estimates; MCMC; Gibbs sampler; Metropolis algorithm; Predictive simulation (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s13198-019-00903-9

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