State estimation for temporal point processes

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This paper is concerned with combined inference for point processes on the real line observed in a broken interval. For such processes, the classic history-based approach cannot be used. Instead, we adapt tools from sequential spatial point processes. For a range of models, the marginal and conditional distributions are derived. We discuss likelihood based inference as well as parameter estimation using the method of moments, conduct a simulation study for the important special case of renewal processes and analyse a data set collected by Diggle and Hawtin.
Original languageUndefined
Place of PublicationEnschede
PublisherUniversity of Twente, Department of Applied Mathematics
Number of pages26
Publication statusPublished - 21 Sep 2015

Publication series

NameMemorandum of the Department of Applied Mathematics
ISSN (Print)1874-4850


  • Markov chain Monte Carlo
  • Cox process
  • Sequential point process
  • State estimation
  • Renewal process
  • EWI-26226
  • MSC-60G55
  • MSC-62M99
  • Markov point process
  • IR-97135
  • MSC-60K15
  • METIS-312699

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