Stein-rule M-estimation in sparse partially linear models

Raheem, Enayetur; Ahmed, S. Ejaz; Liu, Shuangzhe

doi:10.1007/s42081-023-00231-0

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The Proposed Estimators Of R(t)1

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2024

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“…where d is an appropriate nonnegative bounded constant. For more details on Stein-type shrinkage estimators in general and in the context of reliability, see [21][22][23][24][25][26][27][28][29][30].…”

Section: The Proposed Estimators Of R(t)mentioning

confidence: 99%

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

Ahmed,

Belaghi,

Hussein

et al. 2024

Mathematics

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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.

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