Bayesian inference for smooth transition autoregressive (STAR) model: A prior sensitivity analysis. Issue 7 (9th August 2017)

• Record Type: Journal Article
• Authors: Livingston, Glen; Nur, Darfiana
• Is Part Of: Communications in statistics. Volume 46:Issue 7(2017)
• Page Start: 5440
• Page End: 5461
• Publication Date: 2017-08-09
• Subjects: Gibbs Sampler algorithm -- Metropolis-Hastings algorithm -- Multiple Try Metropolis algorithm -- Prior sensitivity analysis -- Reversible Jump MCMC algorithm -- Smooth Transition Autoregressive (STAR) model; 62F15 -- 62M10 -- 65C20 -- 65C40 -- 68U20; Mathematical statistics -- Periodicals; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5
• DOI: 10.1080/03610918.2016.1161794
• Languages: English
• ISSNs: 0361-0918

Abstract

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.