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    Bayesian inference for smooth transition autoregressive (STAR) model: A prior sensitivity analysis

    Access Status
    Fulltext not available
    Authors
    Livingston, G.
    Nur, Darfiana
    Date
    2017
    Type
    Journal Article
    
    Metadata
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    Citation
    Livingston, G. and Nur, D. 2017. Bayesian inference for smooth transition autoregressive (STAR) model: A prior sensitivity analysis. Communications in Statistics: Simulation and Computation. 46 (7): pp. 5440-5461.
    Source Title
    Communications in Statistics: Simulation and Computation
    DOI
    10.1080/03610918.2016.1161794
    ISSN
    0361-0918
    Faculty
    Faculty of Science and Engineering
    School
    School of Elec Eng, Comp and Math Sci (EECMS)
    URI
    http://hdl.handle.net/20.500.11937/79612
    Collection
    • Curtin Research Publications
    Abstract

    The main aim of this paper is to perform sensitivity analysis to the specification of prior distributions in a Bayesian analysis setting of STAR models. To achieve this aim, the joint posterior distribution of model order, coefficient, and implicit parameters in the logistic STAR model is first being presented. The conditional posterior distributions are then shown, followed by the design of a posterior simulator using a combination of Metropolis-Hastings, Gibbs Sampler, RJMCMC, and Multiple Try Metropolis algorithms, respectively. Following this, simulation studies and a case study on the prior sensitivity for the implicit parameters are being detailed at the end.

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