Bayesian inference for smooth transition autoregressive (STAR) model: A prior sensitivity analysis
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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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A Bayesian inference for the penalized spline joint models of longitudinal and time-to-event data: A prior sensitivity analysisThi Thu Pham, H.; Pham, H.; Nur, Darfiana (2020)Bayesian approaches have been used in the literature to estimate the parameters for joint models of longitudinal and time-to-event data. The main aim of this paper is to analyze the impact of prior distributions on ...
Livingston, G.; Nur, Darfiana (2018)© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. The smooth transition autoregressive (STAR)(k)–GARCH(l, m) model is a non-linear time series model that is able to account for changes in both regime and ...
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