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Nonparametric estimation of mean residual lifetime in ranked set sampling with a concomitant variable.
Ehsan Zamanzade1,2, M Mahdizadeh3, Hani M Samawi4
1Department of Statistics, Faculty of Mathematics and Statistics, University of Isfahan, Isfahan, Iran.
Journal of Applied Statistics
|September 18, 2024
Summary
This study introduces new methods for estimating mean residual lifetime (MRL) using ranked set sampling and a concomitant variable. These novel regression-based estimators outperform standard methods when ranking quality is good.
Area of Science:
- Statistics
- Survival Analysis
- Reliability Engineering
Background:
- The mean residual lifetime (MRL) is crucial for predicting future lifespan after a certain time.
- MRL estimation is vital in fields like reliability and survival analysis.
- Existing MRL estimation methods often rely on simple random sampling.
Purpose of the Study:
- To develop and evaluate new MRL estimators using ranked set sampling (RSS).
- To leverage concomitant variable information for improved MRL estimation.
- To compare novel RSS-based MRL estimators against standard methods.
Main Methods:
- Utilized ranked set sampling (RSS) with a concomitant variable.
- Developed several MRL estimators employing regression techniques.
- Conducted comparisons using Monte Carlo simulations and a real-world dataset (SEER Program).
Main Results:
- The proposed MRL estimators based on RSS and regression showed superior performance.
- Effectiveness of the new methods is contingent on good ranking quality in RSS.
- The developed estimators provide more accurate MRL estimates than standard methods in simple random sampling.
Conclusions:
- Regression-based MRL estimators in ranked set sampling offer significant advantages.
- The choice of sampling method and ranking quality critically impacts MRL estimation accuracy.
- This research provides enhanced tools for MRL estimation in survival and reliability analyses.
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