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  • Statistics In Transition


Article | 06-July-2017


The present work explores panel data set-up in a Bayesian state space model. The conditional posterior densities of parameters are utilized to determine the marginal posterior densities using the Gibbs sampler. An efficient one step ahead predictive density mechanism is developed to further the state of art in prediction-based decision making.

Ranjita Pandey, Anoop Chaturvedi

Statistics in Transition New Series, Volume 17 , ISSUE 2, 211–219

Article | 05-September-2021

Bayesian estimation and prediction based on Rayleigh record data with applications

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

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

Statistics in Transition New Series, Volume 22 , ISSUE 3, 59–79

Article | 06-July-2017


autoregression coefficient on the estimation error reduction. The computations performed by ‘sae’ package for R project and a special procedure for WinBUGS reveal that the method provides reliable estimates of small area means. For high spatial correlation between domains, noticeable MSE reduction was observed, which seems more evident for HB-SAR method as compared with the traditional spatial EBLUP. In our opinion, the Gibbs sampler, revealing the simultaneous nature of processes, especially for random

Jan Kubacki, Alina Jędrzejczak

Statistics in Transition New Series, Volume 17 , ISSUE 3, 365–390

Research Article | 13-June-2021

A Bayes algorithm for model compatibility and comparison of ARMA(p,q) models

Praveen Kumar Tripathi, Rijji Sen, S.K. Upadhyay

Statistics in Transition New Series, Volume 22 , ISSUE 2, 95–123

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