Bayesian inference for Smooth Transition Autoregressive (STAR) model: A prior sensitivity analysis

Glen Livingston Jr, Darfiana Nur

    Research output: Contribution to journalArticlepeer-review

    4 Citations (Scopus)


    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.

    Original languageEnglish
    Pages (from-to)5440-5461
    Number of pages22
    Issue number7
    Early online date2017
    Publication statusPublished - 9 Aug 2017


    • Gibbs Sampler algorithm
    • Metropolis-Hastings algorithm
    • Multiple Try Metropolis algorithm
    • Prior sensitivity analysis
    • Reversible Jump MCMC algorithm
    • Smooth Transition Autoregressive (STAR) model

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