Statistical inference of exponential record data under Kullback-Leiber divergence measure

• Authors: Raed R. Abu Awwad , Ghassan K. Abufoudeh , Omar M. Bdair

Abstracts

EN

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 Bayesian point predictor for the future records. Finally, data analyses involving the times (in minutes) to breakdown of an insulating fluid between electrodes at voltage 34 kv have been performed to show the performance of the methods thus developed on estimation and prediction.

Keywords

EN

Bayes estimation; Bayes prediction; record values; Kullback-Leibler divergence measure; exponential distribution

• Publisher: Główny Urząd Statystyczny
• Journal: Statistics in Transition new series
• Year: 2019
• Volume: 20
• Issue: 2
• Pages: 1-14

Contributors

author

Raed R. Abu Awwad

• Department of Mathematics, Faculty of Arts and Sciences, University of Petra, Amman, Jordan

author

Ghassan K. Abufoudeh

• Department of Mathematics, Faculty of Arts and Sciences, University of Petra, Amman, Jordan

author

Omar M. Bdair

• Faculty of Engineering Technology, Al-Balqa Applied University, Amman, Jordan

• https://orcid.org/0000000253464381

Identifiers

DOI

10.21307/stattrans-2019-011

Biblioteka Nauki

1194465