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A Bayesian approach for parameter estimation in multi-stage models

Author

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

Abstract

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.

Suggested Citation

  • Hoa Pham & Darfiana Nur & Huong T. T. Pham & Alan Branford, 2019. "A Bayesian approach for parameter estimation in multi-stage models," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 48(10), pages 2459-2482, May.
    • Handle: RePEc:taf:lstaxx:v:48:y:2019:i:10:p:2459-2482
    DOI: 10.1080/03610926.2018.1465090
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    Cited by:

    1. Pham Hoa & Pham Huong T. T., 2019. "A Bayesian approach for multi-stage models with linear time-dependent hazard rate," Monte Carlo Methods and Applications, De Gruyter, vol. 25(4), pages 307-316, December.

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