Work

Parametric Estimation in a Recurrent Competing Risks Model.

Year: 2013

Type: article

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.

Source: PubMed

Authors: Laura L Taylor, Edsel A Peña

Institutions: Elon University, University of South Carolina

Language: English

Topic: Reliability and Maintenance Optimization

Subfield: Safety, Risk, Reliability and Quality

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funders: National Cancer Institute, National Center for Research Resources

Awards: Stromal modulation of response to Thymidylate synthase inhibitors, Center for Colon Cancer Research

OpenAlex ID: W2125427577

FWCI: 2.829

Cites: 19

Cited by: 5

Related to: 10