Robust estimation of the three parameter Weibull distribution for addressing outliers in reliability analysis

Area of Science:

• Statistical Modeling
• Reliability Engineering
• Data Analysis

Background:

• Accurate parameter estimation is vital for statistical modeling and reliability analysis across industries.
• Traditional methods for the three-parameter Weibull distribution are susceptible to outliers, compromising estimate reliability.
• Outliers pose a significant challenge in analyzing reliability data.

Purpose of the Study:

• To develop a robust estimation technique for the three-parameter Weibull distribution.
• To enhance the accuracy and reliability of parameter estimates in the presence of outliers.
• To provide a computationally simple and easily implementable method for reliability data analysis.

Main Methods:

• A novel robust estimation technique is proposed for the three-parameter Weibull distribution.
• The method utilizes the probability integral transform with the Weibull survival function.
• The focus is on complete data, ensuring applicability in standard reliability scenarios.

Main Results:

• Extensive simulation studies demonstrate the estimator's effectiveness and resilience against outliers.
• The proposed technique significantly improves the accuracy of Weibull parameter estimates compared to traditional methods.
• The method maintains computational simplicity, facilitating practical implementation.

Conclusions:

• The new robust estimation technique offers a valuable improvement for reliability data analysis.
• It effectively addresses the limitations of existing methods when dealing with outliers.
• The practical utility is confirmed through application to real-world reliability datasets.