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Different estimation techniques for constant-partially accelerated life tests of chen distribution using complete

H M M Radwan1, Abdulaziz Alenazi2

  • 1Mathematics Department, Faculty of Science, Minia University, Minia, 61519, Egypt. hmmradwan86@mu.edu.eg.

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|September 20, 2023
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Summary

This study compares estimation techniques for the Chen distribution in accelerated life tests. Maximum product of spacing estimation is most effective for reliability engineering, minimizing bias and error.

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Area of Science:

  • Reliability Engineering
  • Statistical Inference
  • Probability Distributions

Background:

  • Accelerated life testing (ALT) is crucial for product reliability assessment.
  • The Chen distribution models lifetime data effectively under usage conditions.
  • Accurate parameter estimation is vital for predicting product lifespan and performance.

Purpose of the Study:

  • To evaluate various classical estimation techniques for the Chen distribution under constant partially accelerated life tests with complete data.
  • To compare the performance of these techniques in estimating distribution parameters and acceleration factors.
  • To identify the most effective estimation method based on statistical performance metrics.

Main Methods:

  • Maximum Likelihood Estimation (MLE)
  • Least Squares Estimation (LSE) and Weighted Least Squares Estimation (WLSE)
  • Cramér-Von-Mises (CVm), Anderson-Darling (AD), and Right-Tail AD (RAD) estimations
  • Percentile Estimation (PE)
  • Maximum Product of Spacing (MPS) estimation

Main Results:

  • Analysis of two real data sets demonstrated the practical applicability of the proposed estimation techniques in engineering.
  • A simulation study compared techniques using Mean Square Error (MSE) and Absolute Average Bias (AAB).
  • The Maximum Product of Spacing (MPS) estimation method consistently yielded the smallest MSE and AAB values across most scenarios.

Conclusions:

  • The Maximum Product of Spacing (MPS) estimation is the most effective technique for the Chen distribution in constant partially accelerated life tests.
  • The findings provide valuable insights for reliability engineers seeking robust parameter estimation methods.
  • The study confirms the practical utility of advanced statistical methods in addressing engineering challenges related to product lifetime prediction.