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Likelihood based inference for partially observed renewal processes

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  • van Lieshout, M.N.M.

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.

Suggested Citation

  • van Lieshout, M.N.M., 2016. "Likelihood based inference for partially observed renewal processes," Statistics & Probability Letters, Elsevier, vol. 118(C), pages 190-196.
    • Handle: RePEc:eee:stapro:v:118:y:2016:i:c:p:190-196
    DOI: 10.1016/j.spl.2016.07.002
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    References listed on IDEAS

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    1. Anders Brix & Peter J. Diggle, 2001. "Spatiotemporal prediction for log‐Gaussian Cox processes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(4), pages 823-841.
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