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  • Statistics In Transition


Research Article | 08-December-2021

A study on exponentiated Gompertz distribution under Bayesian discipline using informative priors

functions. The findings show that the two best loss functions are the Weighted Balance Loss Function (WBLF) and the Quadratic Loss Function (QLF). The usefulness of the model is illustrated by the use of real life data in relation to simulated data. The empirical results of the comparison are presented through a graphical illustration of the posterior distributions.

Muhammad Aslam, Mehreen Afzaal, M. Ishaq Bhatti

Statistics in Transition New Series, Volume 22 , ISSUE 4, 101–119

Research Article | 08-December-2021

Record data from Kies distribution and related statistical inferences

The Kies probability model was proposed as an alternative to the extended Weibull models as it provides a more efficient fit to some real-life data sets in comparison to the aforementioned models. The paper proposes classical and Bayesian inferences for the Kies distribution based on records. Maximum likelihood estimates are studied jointly with asymptotic and bootstrap confidence intervals. Moreover, Bayes estimates, along with credible intervals are discussed assuming squared and LINEX loss

Nesreen M. Al-Olaimat, Husam A. Bayoud, Mohammad Z. Raqab

Statistics in Transition New Series, Volume 22 , ISSUE 4, 153–170

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 | 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 | 20-December-2020

A new generalization of the Pareto distribution and its applications

estimation. Approximate confidence intervals are obtained by means of an asymptotic property of the maximum likelihood and maximum product spacings methods, while the Bayes credible intervals are found by using the Monte Carlo Markov Chain method under different loss functions. A simulation analysis is conducted to compare the estimation methods. Finally, the application of the proposed new distribution to three real-data examples is presented and its goodness-of-fit is demonstrated. In addition

Ehab M. Almetwally, Hanan A. Haj Ahmad

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

Article | 05-September-2021

Robust Bayesian insurance premium in a collective risk model with distorted priors under the generalised Bregman loss

The article presents a collective risk model for the insurance claims. The objective is to estimate a premium, which is defined as a functional specified up to unknown parameters. For this purpose, the Bayesian methodology, which combines the prior knowledge about certain unknown parameters with the knowledge in the form of a random sample, has been adopted. The generalised Bregman loss function is considered. In effect, the results can be applied to numerous loss functions, including the

Agata Boratyńska

Statistics in Transition New Series, Volume 22 , ISSUE 3, 123–140

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