Bayesian Inference for Switching Mean Models with ARMA errors
- Title
- Bayesian Inference for Switching Mean Models with ARMA errors
- Author
- 김성욱
- Keywords
- witching mean model; multiple change points; ARMA error; noninformative improper prior; fractional Bayes factor; Gibbs sampler; Metropolis-Hastings algorithm
- Issue Date
- 2003-12
- Publisher
- 한국통계학회
- Citation
- CSAM(Communications for Statistical Applications and Methods), v. 10, no. 3, page. 981-996
- Abstract
- Bayesian inference is considered for switching mean models with the ARMA errors.
We use noninformative improper priors or uniform priors.
The fractional Bayes factor of O`Hagan (1995) is used as a Bayesian tool
for detecting the existence of a single change or multiple changes and
the regular Bayes factor is used for identifying the orders of the ARMA error.
Once the model is fully identified, the Gibbs sampler
with the Metropolis-Hastings subchains is constructed to estimate parameters.
Finally, we perform a simulation study to support theoretical results.
- URI
- http://kiss.kstudy.com/thesis/thesis-view.asp?key=2108890https://repository.hanyang.ac.kr/handle/20.500.11754/156729
- ISSN
- 2287-7843
- Appears in Collections:
- COLLEGE OF SCIENCE AND CONVERGENCE TECHNOLOGY[E](과학기술융합대학) > APPLIED MATHEMATICS(응용수학과) > Articles
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