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Type-II progressive censoring with GLM-based random removal mechanism dependent on the experimental conditions
Fatemeh Hassantabar Darzi1, Samaneh Eftekhari Mahabadi1, Firoozeh Haghighi1
1School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, Iran.
Journal of Applied Statistics
|November 16, 2023
Summary
This study introduces a new method for reducing life test experiments using Generalized Linear Models (GLM) for dependent removal probabilities in Type-II progressive censoring. This approach enhances efficiency and reduces costs in reliability studies.
Area of Science:
- Statistics
- Reliability Engineering
- Survival Analysis
Background:
- Life testing experiments are crucial for product reliability but can be time-consuming and costly.
- Traditional censoring methods may not fully capture complex removal scenarios.
- Progressive censoring offers flexibility but often assumes independent removal probabilities.
Purpose of the Study:
- To develop a novel stochastic removal mechanism for Type-II progressive random censoring.
- To incorporate lifetime condition-dependent removal probabilities using Generalized Linear Models (GLM).
- To reduce experimental time and cost while maintaining statistical rigor.
Main Methods:
- Development of a GLM-based random removal mechanism with researcher-defined tuning parameters.
- Application within the Proportional Hazard Rate (PHR) family of distributions.
- Derivation of maximum likelihood estimators and asymptotic variances for Weibull distributed data.
- Simulation algorithm for generating samples with GLM-dependent removals.
- Monte Carlo integration to estimate expected experiment time.
Main Results:
- The proposed GLM-based removal mechanism allows flexible and efficient reduction of experimental duration.
- Simulation studies demonstrate the performance and effectiveness of the new mechanism.
- Sensitivity analysis indicates the impact of misspecified removal coefficients.
- The method is illustrated with real-world data sets.
Conclusions:
- The novel stochastic removal mechanism provides a statistically sound and practical approach to optimize life testing experiments.
- GLM-based dependent removals offer significant advantages in cost and time efficiency for reliability analysis.
- The developed methods and simulations are valuable tools for researchers in survival analysis and reliability engineering.
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