Abstract | ||
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Many applications, in particular the failure, repair, and replacement of industrial components or physical infrastructure, involve recurrent events. Frequently, the available data are window-censored: only events that occurred during a particular interval are recorded. Window censoring presents a challenge for recurrence data analysis. For statistical inference from window censored recurrence data, we derive the likelihood function for a model in which the distributions of inter-recurrence intervals in a single path need not be identical and may be associated with covariate information. We assume independence among different sample paths. We propose a distribution to model the effect of external interventions on recurrence processes. This distribution can represent a phenomenon, frequently observed in practice, that the probability of process regeneration increases with the number of historical interventions; for example, an item that had a given number of repairs is generally more likely to be replaced in the wake of a failure than a similar item with a smaller number of repairs. The proposed model and estimation procedure are evaluated via simulation studies and applied to a set of data related to failure and maintenance of water mains. This article has online supplementary material. |
Year | DOI | Venue |
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2014 | 10.1080/00401706.2013.804442 | TECHNOMETRICS |
Keywords | Field | DocType |
Failure probability,Infrastructure reliability,Lognormal,Maintenance policy,Markov renewal process,Maximum likelihood,Weibull | Econometrics,Covariate,Likelihood function,Data quality,Weibull distribution,Statistical inference,Statistics,Log-normal distribution,Censoring (statistics),Mathematics,Markov renewal process | Journal |
Volume | Issue | ISSN |
56.0 | 1.0 | 0040-1706 |
Citations | PageRank | References |
3 | 0.55 | 4 |
Authors | ||
3 |
Name | Order | Citations | PageRank |
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Yada Zhu | 1 | 39 | 10.49 |
Emmanuel Yashchin | 2 | 14 | 3.70 |
J. R. M. Hosking | 3 | 6 | 1.85 |