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

Glen Livingston Jr; Darfiana Nur

doi:10.1080/03610918.2016.1161794

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

Glen Livingston Jr, Darfiana Nur

Research output: Contribution to journal › Article › peer-review

4 Citations (Scopus)

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.

Original language	English
Pages (from-to)	5440-5461
Number of pages	22
Journal	COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
Volume	46
Issue number	7
Early online date	2017
DOIs	https://doi.org/10.1080/03610918.2016.1161794
Publication status	Published - 9 Aug 2017

Keywords

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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title = "Bayesian inference for Smooth Transition Autoregressive (STAR) model: A prior sensitivity analysis",

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.",

keywords = "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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KW - Metropolis-Hastings algorithm

KW - Multiple Try Metropolis algorithm

KW - Prior sensitivity analysis

KW - Reversible Jump MCMC algorithm

KW - Smooth Transition Autoregressive (STAR) model

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SN - 0361-0918

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ER -

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

Abstract

Keywords

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