Article | 06-July-2017
In this study the benefits arising from the use of the Bayesian approach to predictive modelling will be outlined and exemplified by a linear regression model and a logistic regression model. The impact of informative and non-informative prior on model accuracy will be examined and compared. The data from the Central Statistical Office of Poland describing unemployment in individual districts in Poland will be used. Markov Chain Monte Carlo methods (MCMC) will be employed in modelling.
Wioletta Grzenda
Statistics in Transition New Series, Volume 17 , ISSUE 4, 763–780
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 | 20-December-2020
Varun Agiwal,
Jitendra Kumar,
Dahud Kehinde Shangodoyin
Statistics in Transition New Series, Volume 21 , ISSUE 5, 133–149
Sampling Methods | 25-May-2018
Varun Agiwal,
Jitendra Kumar,
Dahud Kehinde Shangodoyin
Statistics in Transition New Series, Volume 19 , ISSUE 1, 7–23
Article | 15-March-2019
Ping Gao,
Hikaru Hasegawa
Statistics in Transition New Series, Volume 19 , ISSUE 4, 645–670
Research Communicate | 27-May-2018
Jan Kordos
Statistics in Transition New Series, Volume 19 , ISSUE 1, 149–158