Bayesian Estimation and Prediction Based on Rayleigh Record Data with Applications

• Autorzy: Raed R. Abu Awwad , Omar M. Bdair , Ghassan K. Abufoudeh
• Języki publikacji: EN

Abstrakty

EN

Based on a record sample from the Rayleigh model, we consider the problem of estimatingthe scale and location parameters of the model and predicting the future unobserved recorddata. Maximum likelihood and Bayesian approaches under different loss functions are usedto estimate the model's parameters. The Gibbs sampler and Metropolis-Hastings methodsare 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 futurerecord data. Monte Carlo simulations are performed to study the behaviour and to comparemethods obtained in this way. Two examples of real data have been analyzed to illustrate theprocedures developed here.(original abstract)

Słowa kluczowe

EN

Bayesian estimation; Monte Carlo method; Markov chain

PL

Estymacja bayesowska; Metoda Monte Carlo; Łańcuch Markowa

• Czasopismo: Statistics in Transition
• Rocznik: 2021
• Tom: 22
• Numer: nr 3
• Strony: 59--79

Twórcy

autor

Raed R. Abu Awwad

• University of Petra, Amman, Jordan

autor

Omar M. Bdair

• Al-Balqa Applied University, Amman, Jordan

autor

Ghassan K. Abufoudeh

• University of Petra, Amman, Jordan

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Identyfikatory

DOI

10.21307/stattrans-2021-027