Enhancing Overlap Measures in Censored Data Analysis: A Focus on Pareto Distributions and Adaptive Type-II Censoring
Keywords:
Bootstrap method; Overlap measures; adaptive type-II progressive hybrid censoring
Abstract
This article explores the application of the adaptive type-II progressive hybrid censoring scheme to calculate three widely recognized statistical measures of overlap for two Pareto distributions with distinct parameters. By utilizing this innovative censoring technique, we derive estimators for these measures and provide their asymptotic bias and variance. When small sample sizes pose challenges in assessing estimator precision or bias due to the lack of closed-form expressions for variances and exact sampling distributions, Monte Carlo simulations are employed to enhance reliability. Additionally, we construct confidence intervals for these measures using both the bootstrap method and Taylor approximation. Our approach offers improved accuracy and efficiency in estimating overlap measures, addressing key challenges in censored data analysis. To demonstrate the practical relevance of our proposed estimators, we include an illustrative application involving real data from an Iranian insurance company.References
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2) Alhamidah, A., Qmi, M. N., \& Kiapour, A. (2023). Comparison of E-Bayesian estimators in Burr XII model using E-PMSE based on record values. Statistics, Optimization \& Information Computing, 11(3), 709-718.
3) Alslman, M., \& Helu, A. (2023). Reliability Estimation for the Inverse Weibull Distribution Under Adaptive Type-II Progressive Hybrid
Censoring: Comparative Study. Statistics, Optimization \& Information Computing, 11(2), 216-242.
4) Helu, A. (2024). Overlap Analysis in Progressive Hybrid Censoring: A Focus on Adaptive Type-II and Lomax Distribution. Statistics, Optimization \& Information Computing.
Published
2025-02-20
How to Cite
Helu, A. (2025). Enhancing Overlap Measures in Censored Data Analysis: A Focus on Pareto Distributions and Adaptive Type-II Censoring. Statistics, Optimization & Information Computing. https://doi.org/10.19139/soic-2310-5070-2222
Issue
Section
Research Articles
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