Bayesian estimation and prediction based on Rayleigh record data with applications

• Authors: Raed R. Abu Awwad , Omar M. Bdair , Ghassan K. Abufoudeh

Abstracts

EN

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

EN

Bayesian estimation and prediction; Rayleigh distribution; record values; Markov Chain Monte Carlo samples

• Publisher: Główny Urząd Statystyczny
• Journal: Statistics in Transition new series
• Year: 2021
• Volume: 22
• Issue: 3
• Pages: 59-79

Contributors

author

Raed R. Abu Awwad

• Department of Mathematics, Faculty of Arts and Sciences, University of Petra, Amman, Jordan

author

Omar M. Bdair

• Faculty of Engineering Technology, Al-Balqa Applied University, Amman, Jordan
• Department of Mathematics and Statistics, McMaster University, Hamilton, Canada

author

Ghassan K. Abufoudeh

• Department of Mathematics, Faculty of Arts and Sciences, University of Petra, Amman, Jordan

Identifiers

DOI

10.21307/stattrans-2021-027

Biblioteka Nauki

1827533