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
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
Praveen Kumar Tripathi,
Rijji Sen,
S.K. Upadhyay
Statistics in Transition New Series, Volume 22 , ISSUE 2, 95–123