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

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

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 bein...

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Bibliographic Details
Main Authors: Livingston, G., Nur, Darfiana
Format: Journal Article
Language:English
Published: TAYLOR & FRANCIS INC 2017
Subjects:
Science & Technology
Physical Sciences
Statistics & Probability
Mathematics
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
TIME-SERIES
VARIABLE SELECTION
Online Access:http://hdl.handle.net/20.500.11937/79612

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