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Nonparametric inference for a class of semi-Markov processes with censored observations. (English) Zbl 0552.62020
A class of semi-Markov models, those which have proportional hazards and which are forward-going (if state j can be reached from i, then i cannot be reached from j), are shown to fit into the multiplicative intensity model of counting processes after suitable random time changes. Standard large-sample results for counting processes following this multiplicative model can therefore be used to make inferences on the above class of semi-Markov models, including the case where observations may be censored. Large-sample results for a four-state model used in clinical trials are presented. (Authors’ summary)
Reviewer: A.Földes

MSC:
62G05 Nonparametric estimation
62M05 Markov processes: estimation; hidden Markov models
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