Likelihood based inference for partially observed renewal processes

M.N.M. van Lieshout

doi:10.1016/j.spl.2016.07.002

Likelihood based inference for partially observed renewal processes

M.N.M. van Lieshout^*

^*Corresponding author for this work

Stochastic Operations Research

Research output: Contribution to journal › Article › Academic › peer-review

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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.

Original language	English
Pages (from-to)	190-196
Number of pages	7
Journal	Statistics & probability letters
Volume	118
DOIs	https://doi.org/10.1016/j.spl.2016.07.002
Publication status	Published - Nov 2016

Keywords

Renewal process
State estimation
Sequential point process
Markov chain Monte Carlo
2023 OA procedure

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10.1016/j.spl.2016.07.002

ArticleFinal published version, 424 KBLicence: Taverne

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title = "Likelihood based inference for partially observed renewal processes",

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.",

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AB - 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.

KW - Renewal process

KW - State estimation

KW - Sequential point process

KW - Markov chain Monte Carlo

KW - 2023 OA procedure

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DO - 10.1016/j.spl.2016.07.002

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JO - Statistics & probability letters

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Likelihood based inference for partially observed renewal processes

Abstract

Keywords

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