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  • Statistics In Transition


Article | 22-July-2019


Based on one parameter exponential record data, we conduct statistical inferences (maximum likelihood estimator and Bayesian estimator) for the suggested model parameter. Our second aim is to predict the future (unobserved) records and to construct their corresponding prediction intervals based on observed set of records. In the estimation and prediction processes, we consider the square error loss and the Kullback-Leibler loss functions. Numerical simulations were conducted to evaluate the

Raed r. . Abu Awwad

Statistics in Transition New Series, Volume 20 , ISSUE 2, 1–14

Article | 20-December-2020

A new generalization of the Pareto distribution and its applications

Ehab M. Almetwally, Hanan A. Haj Ahmad

Statistics in Transition New Series, Volume 21 , ISSUE 5, 61–84

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

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