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Rainfall data modeling using improved adaptive type-II progressively censored Weibull-exponential samples.

Refah Alotaibi1, Mazen Nassar2,3, Ahmed Elshahhat4

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This summary is machine-generated.

This study introduces an improved adaptive Type-II progressive censoring technique for long-term experiments. It compares traditional and Bayesian estimation methods for Weibull-exponential distribution parameters and reliability indicators.

Keywords:
Bayesian estimationImproved adaptive progressiveInterval estimationLikelihood estimationReliability estimationWeibull-exponential

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Area of Science:

  • Statistics
  • Reliability Engineering
  • Survival Analysis

Background:

  • Long-term experiments require efficient data collection methods.
  • Progressive censoring offers an alternative to complete data collection.
  • The Weibull-exponential distribution is frequently used in reliability studies.

Purpose of the Study:

  • To develop and evaluate an improved adaptive Type-II progressive censoring scheme.
  • To compare frequentist and Bayesian estimation techniques for reliability parameters under this scheme.
  • To assess the performance of different censoring plans using real-world data.

Main Methods:

  • Utilizing an improved adaptive Type-II progressive censoring technique.
  • Applying maximum likelihood estimation for frequentist parameter and interval estimation.
  • Employing Markov chain Monte Carlo (MCMC) methods for Bayesian estimation and credible intervals.
  • Conducting simulation studies to compare estimation methods under various conditions.
  • Analyzing rainfall data sets to demonstrate practical application and select optimal censoring plans.

Main Results:

  • The study provides both point and interval estimates using traditional and Bayesian approaches.
  • Simulation analysis helps differentiate the performance of the two estimation methodologies.
  • The effectiveness of the proposed censoring technique is demonstrated through application to rainfall data.
  • Precision criteria are used to identify the most efficient progressive censoring plan.

Conclusions:

  • The improved adaptive Type-II progressive censoring technique offers a viable approach for long-term studies.
  • Both frequentist and Bayesian methods provide valuable insights into parameter and reliability estimation.
  • The choice between methods and censoring plans depends on specific experimental conditions and objectives.
  • The study contributes to the field of statistical inference for reliability analysis.