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Bayesian and Classical Inference under Type-II Censored Samples of the Extended Inverse Gompertz Distribution with

Ahmed Elshahhat1, Hassan M Aljohani2, Ahmed Z Afify3

  • 1Faculty of Technology and Development, Zagazig University, Zagazig 44519, Egypt.

Entropy (Basel, Switzerland)
|December 24, 2021
PubMed
Summary
This summary is machine-generated.

Researchers developed a new three-parameter extended inverse-Gompertz (EIGo) distribution for reliability analysis. This flexible statistical model offers improved fits for engineering data compared to existing inverted distributions.

Keywords:
Bayesian estimationMCMCType-II censored dataentropiesinverse-Gompertz distributionmaximum likelihood estimationmomentsstress-strength reliability

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Area of Science:

  • Statistics
  • Reliability Engineering
  • Probability Distributions

Background:

  • Existing statistical distributions may not adequately capture complex failure patterns in engineering applications.
  • There is a need for flexible probability models with adaptable characteristics for reliability analysis.

Purpose of the Study:

  • Introduce and characterize a novel three-parameter distribution: the extended inverse-Gompertz (EIGo) distribution.
  • Investigate the statistical and reliability properties of the EIGo distribution.
  • Evaluate the performance of parameter estimation methods for the EIGo distribution.

Main Methods:

  • The extended inverse-Gompertz (EIGo) distribution is proposed as a flexible model.
  • Statistical and reliability properties, including the failure rate function (upside-down bathtub shape), are derived.
  • Parameter estimation is performed using maximum likelihood and Bayesian methods with gamma priors under Type-II censoring.
  • Simulation studies are conducted to assess the performance of estimation techniques.

Main Results:

  • The EIGo distribution demonstrated superior fitting capabilities compared to several established inverted distributions.
  • Parameter estimation methods showed good performance in simulation studies.
  • The EIGo distribution effectively models real-life engineering data.

Conclusions:

  • The extended inverse-Gompertz (EIGo) distribution is a viable and effective model for reliability analysis.
  • The proposed distribution offers a valuable alternative to existing models, particularly for datasets exhibiting complex failure behaviors.
  • The study validates the applicability and superiority of the EIGo distribution in practical engineering contexts.