A Bayesian approach for parameter estimation in multi-stage models

Hoa Pham, Darfiana Nur, Huong T.T. Pham, Alan Branford

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


Multi-stage time evolving models are common statistical models for biological systems, especially insect populations. In stage-duration distribution models, parameter estimation for the models use the Laplace transform method. This method involves assumptions such as known constant shapes, known constant rates or the same overall hazard rate for all stages. These assumptions are strong and restrictive. The main aim of this paper is to weaken these assumptions by using a Bayesian approach. In particular, a Metropolis-Hastings algorithm based on deterministic transformations is used to estimate parameters. We will use two models, one which has no hazard rates, and the other has stage-wise constant hazard rates. These methods are validated in simulation studies followed by a case study of cattle parasites. The results show that the proposed methods are able to estimate the parameters comparably well, as opposed to using the Laplace transform methods.

Original languageEnglish
Pages (from-to)2459-2482
Number of pages24
JournalCommunications in Statistics - Theory and Methods
Issue number10
Publication statusPublished - 19 May 2019


  • Bayesian analysis
  • destructive samples
  • multi-stage models
  • stage duration
  • stage frequency data


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