Volume 12, Issue 1 (March 2013)                   JIRSS 2013, 12(1): 153-181 | Back to browse issues page

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Taylor L L, Pen ̃a E A. Parametric Estimation in a Recurrent Competing Risks Model. JIRSS. 2013; 12 (1) :153-181
URL: http://jirss.irstat.ir/article-1-213-en.html
Abstract:   (7039 Views)
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 compet- ing 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 estima- tors of the parameters of the marginal distribution functions associated with each of the competing risks and also of the system lifetime dis- tribution 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.
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Received: 2013/04/13 | Accepted: 2015/09/12 | Published: 2013/03/15

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