Titman, Andrew (2013) Estimating parametric semi-Markov models from panel data using phase-type approximations. Statistics and Computing. ISSN 0960-3174
Full text not available from this repository.Abstract
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: | Article |
|---|---|
| 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 |
| ID Code: | 58810 |
| Deposited By: | ep_importer_pure |
| Deposited On: | 03 Oct 2012 13:10 |
| Refereed?: | Yes |
| Published?: | Published |
| Last Modified: | 11 Apr 2013 14:32 |
| Identification Number: | |
| URI: | http://eprints.lancs.ac.uk/id/eprint/58810 |
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