Likelihood based inference for partially observed renewal processes

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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.
Original languageUndefined
Pages (from-to)190-196
Number of pages7
JournalStatistics & probability letters
Publication statusPublished - Nov 2016


  • Renewal process
  • State estimation
  • EWI-27111
  • IR-101013
  • Sequential point process
  • METIS-318476
  • Markov chain Monte Carlo

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