Likelihood for generally coarsened observations from multi-state or counting process models

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dc.creatorCommenges, Daniel
dc.creatorGégout-Petit, Anne
dc.date2008-05-23
dc.date.accessioned2026-07-07T12:19:12Z
dc.date.available2026-07-07T12:19:12Z
dc.descriptionWe consider first the mixed discrete-continuous scheme of observation in multistate models; this is a classical pattern in epidemiology because very often clinical status is assessed at discrete visit times while times of death or other events are observed exactly. A heuristic likelihood can be written for such models, at least for Markov models; however, a formal proof is not easy and has not been given yet. We present a general class of possibly non-Markov multistate models which can be represented naturally as multivariate counting processes. We give a rigorous derivation of the likelihood based on applying Jacod's formula for the full likelihood and taking conditional expectation for the observed likelihood. A local description of the likelihood allows us to extend the result to a more general coarsening observation scheme proposed by Commenges & Gégout-Petit. The approach is illustrated by considering models for dementia, institutionalization and death.
dc.identifierhttps://arxiv.org/abs/0805.3658
dc.identifierhttp://arxiv.org/abs/0805.3658
dc.identifierScandinavian Journal of Statistics 34, 2 (2007) 432-450
dc.identifierdoi:10.1111/j.1467-9469.2006.00518.x
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/212669
dc.subjectStatistics Theory
dc.titleLikelihood for generally coarsened observations from multi-state or counting process models
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