New and Efficient Estimators of Reliability Characteristics for a Family of Lifetime Distributions under Progressive Censoring

Abstract

Estimation of reliability and stress-strength parameters is important in the manufacturing industry. In this paper, we develop shrinkage-type estimators for the reliability and stress-strength parameters based on progressively censored data from a rich class of distributions. These new estimators improve the performance of the commonly used Maximum Likelihood Estimators (MLEs) by reducing their mean squared errors. We provide analytical asymptotic and bootstrap confidence intervals for the targeted parameters. Through a detailed simulation study, we demonstrate that the new estimators have better performance than the MLEs. Finally, we illustrate the application of the new methods to two industrial data sets, showcasing their practical relevance and effectiveness.

Keywords

bootstrap; lifetime; preliminary test; progressive censoring; reliability; stress-strength; Stein-type shrinkage estimators

Published in

Mathematics
2024, volume: 12, number: 10, article number: 1599
Publisher: MDPI

SLU Authors

• Belaghi, Reza

  • Department of Energy and Technology, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Probability Theory and Statistics

Publication identifier

• DOI: https://doi.org/10.3390/math12101599

Permanent link to this page (URI)

https://res.slu.se/id/publ/142353