Likelihood based inference for partially observed renewal processes

M.N.M. van Lieshout

Statistics & Probability Letters, 2016, vol. 118, issue C, 190-196

Abstract: This paper is concerned with inference for renewal processes on the real line that are observed in a broken interval. For such processes, the classic history-based approach cannot be used. Instead, we adapt tools from sequential spatial point process theory to propose a Monte Carlo maximum likelihood estimator that takes into account the missing data. Its efficacy is assessed by means of a simulation study and the missing data reconstruction is illustrated on real data.

Keywords: Markov chain Monte Carlo; Renewal process; Sequential point process; State estimation (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:eee:stapro:v:118:y:2016:i:c:p:190-196

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DOI: 10.1016/j.spl.2016.07.002

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