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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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:
Journal Article
Journal or Publication Title:
Statistics and Computing
Uncontrolled Keywords:
/dk/atira/pure/researchoutput/libraryofcongress/qa
Subjects:
ID Code:
58810
Deposited By:
Deposited On:
03 Oct 2012 12:10
Refereed?:
Yes
Published?:
Published
Last Modified:
07 Jan 2020 03:42