Parametric Estimation in a Recurrent Competing Risks Model.

IF 0.1 Q4 STATISTICS & PROBABILITY
Laura L Taylor, Edsel A Peña
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Abstract

A resource-efficient approach to making inferences about the distributional properties of the failure times in a competing risks setting is presented. Efficiency is gained by observing recurrences of the competing risks over a random monitoring period. The resulting model is called the recurrent competing risks model (RCRM) and is coupled with two repair strategies whenever the system fails. Maximum likelihood estimators of the parameters of the marginal distribution functions associated with each of the competing risks and also of the system lifetime distribution function are presented. Estimators are derived under perfect and partial repair strategies. Consistency and asymptotic properties of the estimators are obtained. The estimation methods are applied to a data set of failures for cars under warranty. Simulation studies are used to ascertain the small sample properties and the efficiency gains of the resulting estimators.

Abstract Image

Abstract Image

循环竞争风险模型中的参数估计。
本文提出了一种资源节约型方法,用于推断竞争风险环境下故障时间的分布特性。通过观察随机监测期内竞争风险的复发情况来提高效率。由此产生的模型被称为复发性竞争风险模型(RCRM),并与系统故障时的两种修复策略相结合。本文提出了与每种竞争风险相关的边际分布函数参数以及系统寿命分布函数参数的最大似然估计值。在完全修复和部分修复策略下推导出估计值。获得了估计值的一致性和渐近特性。估算方法适用于保修期内汽车的故障数据集。通过模拟研究,确定了小样本特性以及由此得出的估算器的效率增益。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
1.50
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