Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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Parameter estimation of an extended inverse power Lomax distribution with Type I right censored data

  • Hassan, Amal S. (Department of Mathematical Statistics, Cairo University) ;
  • Nassr, Said G. (Department of Quantitative Methods, Sinai University)
  • Received : 2019.12.24
  • Accepted : 2021.01.08
  • Published : 2021.03.31
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Abstract

In this paper, we introduce an extended form of the inverse power Lomax model via Marshall-Olkin approach. We call it the Marshall-Olkin inverse power Lomax (MOIPL) distribution. The four- parameter MOIPL distribution is very flexible which contains some former and new models. Vital properties of the MOIPL distribution are affirmed. Maximum likelihood estimators and approximate confidence intervals are considered under Type I censored samples. Maximum likelihood estimates are evaluated according to simulation study. Bayesian estimators as well as Bayesian credible intervals under symmetric loss function are obtained via Markov chain Monte Carlo (MCMC) approach. Finally, the flexibility of the new model is analyzed by means of two real data sets. It is found that the MOIPL model provides closer fits than some other models based on the selected criteria.

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Acknowledgement

The authors are grateful to the editor and referees for their useful comments and suggestions for improvement which undoubtedly enhanced the presentation and quality of the article.

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