Titman, Andrew (2014) Estimating parametric semi-Markov models from panel data using phase-type approximations. Statistics and Computing, 24 (2). pp. 155-164. ISSN 0960-3174Full text not available from this repository.
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.
|Journal or Publication Title:||Statistics and Computing|
|Uncontrolled Keywords:||B-splines ; gamma distribution ; Hidden Markov model ; misclassification ; Panel data ; Phase-type distribution ; Semi-Markov ; Weibull|
|Subjects:||Q Science > QA Mathematics|
|Departments:||Faculty of Science and Technology > Mathematics and Statistics|
|Deposited On:||03 Oct 2012 13:10|
|Last Modified:||26 Mar 2017 00:36|
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