Bayesian estimation and prediction based on Rayleigh record data with applications

Abu Awwad Raed R. (), Bdair Omar M. () and Abufoudeh Ghassan K. ()
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Abu Awwad Raed R.: Department of Mathematics, Faculty of Arts and Sciences, University of Petra, Amman, Jordan .
Bdair Omar M.: Faculty of Engineering Technology, Al-Balqa Applied University, Amman, 11134, Jordan . Department of Mathematics and Statistics, McMaster University, Hamilton, ON, L8S 4L8, Canada .
Abufoudeh Ghassan K.: Department of Mathematics, Faculty of Arts and Sciences, University of Petra, Amman, Jordan .

Statistics in Transition New Series, 2021, vol. 22, issue 3, 59-79

Abstract: Based on a record sample from the Rayleigh model, we consider the problem of estimating the scale and location parameters of the model and predicting the future unobserved record data. Maximum likelihood and Bayesian approaches under different loss functions are used to estimate the model’s parameters. The Gibbs sampler and Metropolis-Hastings methods are used within the Bayesian procedures to draw the Markov Chain Monte Carlo (MCMC) samples, used in turn to compute the Bayes estimator and the point predictors of the future record data. Monte Carlo simulations are performed to study the behaviour and to compare methods obtained in this way. Two examples of real data have been analyzed to illustrate the procedures developed here.

Keywords: Bayesian estimation and prediction; Rayleigh distribution; record values; Markov Chain Monte Carlo samples. (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:stintr:v:22:y:2021:i:3:p:59-79:n:5

DOI: 10.21307/stattrans-2021-027

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