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  • Statistics In Transition

 

Article | 06-July-2017

INFORMATIVE VERSUS NON-INFORMATIVE PRIOR DISTRIBUTIONS AND THEIR IMPACT ON THE ACCURACY OF BAYESIAN INFERENCE

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

Modelling the occupational and educational choices of young people in Poland using Bayesian multinomial logit models

were estimated within the Bayesian approach. The findings show that continuing education by young people may result from their problems with finding a job; moreover, combining work with education is not the preferred form of professional activity. In addition, the study examines the inequalities observed on the Polish labour market.

Wioletta Grzenda

Statistics in Transition New Series, Volume 22 , ISSUE 3, 175–191

Article | 20-December-2020

Comparing particulate matter dispersion in Thailand using the Bayesian Confidence Intervals for ratio of coefficients of variation

statistical inference on the coefficient of variation. In this paper, we develop confidence interval estimation for the ratio of coefficients of variation of two log-normal distributions constructed using the Bayesian approach. These confidence intervals were then compared with the existing approaches: method of variance estimates recovery (MOVER), modified MOVER, and approximate fiducial approaches using their coverage probabilities and average lengths via Monte Carlo simulation. The simulation results

Warisa Thangjai, Suparat Niwitpong

Statistics in Transition New Series, Volume 21 , ISSUE 5, 41–60

Article | 15-September-2020

A general Bayesian approach to meet different inferential goals in poverty research for small areas

Partha Lahiri, Jiraphan Suntornchost

Statistics in Transition New Series, Volume 21 , ISSUE 4, 237–253

Article | 20-December-2020

A Bayesian analysis of complete multiple breaks in a panel autoregressive (CMB-PAR(1)) time series model

panel autoregressive model with multiple breaks present in all parameters, i.e. in the autoregressive coefficient and mean and error variance, which is a generalisation of various sub-models. The Bayesian approach is applied to estimate the model parameters and to obtain the highest posterior density interval. Strong evidence is observed to support the Bayes estimator and then it is compared with the maximum likelihood estimator. A simulation experiment is conducted and an empirical application on

Varun Agiwal, Jitendra Kumar, Dahud Kehinde Shangodoyin

Statistics in Transition New Series, Volume 21 , ISSUE 5, 133–149

Article | 06-July-2017

A TWO-COMPONENT NORMAL MIXTURE ALTERNATIVE TO THE FAY-HERRIOT MODEL

This article considers a robust hierarchical Bayesian approach to deal with random effects of small area means when some of these effects assume extreme values, resulting in outliers. In the presence of outliers, the standard Fay-Herriot model, used for modeling area-level data, under normality assumptions of random effects may overestimate the random effects variance, thus providing less than ideal shrinkage towards the synthetic regression predictions and inhibiting the borrowing of

Adrijo Chakraborty, Gauri Sankar Datta, Abhyuday Mandal

Statistics in Transition New Series, Volume 17 , ISSUE 1, 67–90

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