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    A Bayesian approach for parameter estimation in multi-stage models

    Access Status
    Fulltext not available
    Authors
    Pham, Hoa
    Nur, Darfiana
    Pham, Huong TT
    Branford, Alan
    Date
    2019
    Type
    Journal Article
    
    Metadata
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    Citation
    Pham, H. and Nur, D. and Pham, H.T.T. and Branford, A. 2019. A Bayesian approach for parameter estimation in multi-stage models. Communications in Statistics-Theory and Methods. 48 (10): pp. 2459-2482.
    Source Title
    Communications in Statistics-Theory and Methods
    DOI
    10.1080/03610926.2018.1465090
    ISSN
    0361-0926
    Faculty
    Faculty of Science and Engineering
    School
    School of Elec Eng, Comp and Math Sci (EECMS)
    URI
    http://hdl.handle.net/20.500.11937/79606
    Collection
    • Curtin Research Publications
    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 stagewise 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.

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