Estimating parametric semi-Markov models from panel data using phase-type approximations

Titman, Andrew (2014) Estimating parametric semi-Markov models from panel data using phase-type approximations. Statistics and Computing, 24 (2). pp. 155-164. ISSN 0960-3174

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Inference for semi-Markov models under panel data presents considerable computational difficulties. In general the likelihood is intractable, but a tractable likelihood with the form of a hidden Markov model can be obtained if the sojourn times in each of the states are assumed to have phase-type distributions. However, using phase-type distributions directly may be undesirable as they require estimation of parameters which may be poorly identified. In this article, an approach to fitting semi-Markov models with standard parametric sojourn distributions is developed. The method involves establishing a family of Coxian phase-type distribution approximations to the parametric distribution and merging approximations for different states to obtain an approximate semi-Markov process with a tractable likelihood. Approximations are developed for Weibull and Gamma distributions and demonstrated on data relating to post-lung-transplantation patients.

Item Type:
Journal Article
Journal or Publication Title:
Statistics and Computing
Uncontrolled Keywords:
?? b-splines gamma distributionhidden markov modelmisclassificationpanel dataphase-type distributionsemi-markovweibullmathematics and statisticscomputational theory and mathematicstheoretical computer sciencestatistics and probabilitystatistics, probability ??
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Deposited On:
03 Oct 2012 12:10
Last Modified:
15 Jul 2024 13:18