Bayesian single changepoint estimation in a parameter-driven model
Bayesian single changepoint estimation in a parameter-driven model
In this paper, we consider the problem of estimating a single changepoint in a parameter-driven model. The model – an extension of the Poisson regression model – accounts for serial correlation through a latent process incorporated in its mean function. Emphasis is placed on the changepoint characterization with changes in the parameters of the model. The model is fully implemented within the Bayesian framework. We develop a RJMCMC algorithm for parameter estimation and model determination. The algorithm embeds well-devised Metropolis–Hastings procedures for estimating the missing values of the latent process through data augmentation and the changepoint. The methodology is illustrated using data on monthly counts of claimants collecting wage loss benefit for injuries in the workplace and an analysis of presidential uses of force in the USA.
count data, data augmentation, latent process, Poisson distribution, reversible jumpMCMC
765-779
Utazi, Chigozie
e69ca81e-fb23-4bc1-99a5-25c9e0f4d6f9
Utazi, Chigozie
e69ca81e-fb23-4bc1-99a5-25c9e0f4d6f9
Utazi, Chigozie
(2017)
Bayesian single changepoint estimation in a parameter-driven model.
Scandinavian Journal of Statistics, 44 (3), .
(doi:10.1111/sjos.12274).
Abstract
In this paper, we consider the problem of estimating a single changepoint in a parameter-driven model. The model – an extension of the Poisson regression model – accounts for serial correlation through a latent process incorporated in its mean function. Emphasis is placed on the changepoint characterization with changes in the parameters of the model. The model is fully implemented within the Bayesian framework. We develop a RJMCMC algorithm for parameter estimation and model determination. The algorithm embeds well-devised Metropolis–Hastings procedures for estimating the missing values of the latent process through data augmentation and the changepoint. The methodology is illustrated using data on monthly counts of claimants collecting wage loss benefit for injuries in the workplace and an analysis of presidential uses of force in the USA.
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SJS_accepted
- Accepted Manuscript
More information
Accepted/In Press date: 7 November 2016
e-pub ahead of print date: 24 March 2017
Keywords:
count data, data augmentation, latent process, Poisson distribution, reversible jumpMCMC
Organisations:
Statistics, WorldPop
Identifiers
Local EPrints ID: 407421
URI: http://eprints.soton.ac.uk/id/eprint/407421
ISSN: 0303-6898
PURE UUID: 5f6c14fc-8d9f-452b-9abc-181e467053dc
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Date deposited: 07 Apr 2017 01:02
Last modified: 16 Mar 2024 05:14
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