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

Title: Bayesian inference for smooth transition autoregressive (STAR) model: A prior sensitivity analysis
Creator: Livingston Jr, Glen; Nur, Darfiana
Relation: Communications in Statistics - Simulation and Computation Vol. 46, Issue 7, p. 5440-5461
Publisher Link: http://dx.doi.org/10.1080/03610918.2016.1161794
Publisher: Taylor & Francis
Resource Type: journal article
Date: 2017
Description: 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.
Subject: Gibbs Sampler algorithm; Metropolis-Hastings algorithm; Multiple Try Metropolis algorithm; prior sensitivity analysis; reversible jump MCMC algorithm; smooth transition autoregressive (STAR) model
Identifier: http://hdl.handle.net/1959.13/1459170
Identifier: uon:45597
Identifier: ISSN:0361-0918
Language: eng
Reviewed