Open Access
September 2019 Semiparametric Multivariate and Multiple Change-Point Modeling
Stefano Peluso, Siddhartha Chib, Antonietta Mira
Bayesian Anal. 14(3): 727-751 (September 2019). DOI: 10.1214/18-BA1125


We develop a general Bayesian semiparametric change-point model in which separate groups of structural parameters (for example, location and dispersion parameters) can each follow a separate multiple change-point process, driven by time-dependent transition matrices among the latent regimes. The distribution of the observations within regimes is unknown and given by a Dirichlet process mixture prior. The properties of the proposed model are studied theoretically through the analysis of inter-arrival times and of the number of change-points in a given time interval. The prior-posterior analysis by Markov chain Monte Carlo techniques is developed on a forward-backward algorithm for sampling the various regime indicators. Analysis with simulated data under various scenarios and an application to short-term interest rates are used to show the generality and usefulness of the proposed model.


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Stefano Peluso. Siddhartha Chib. Antonietta Mira. "Semiparametric Multivariate and Multiple Change-Point Modeling." Bayesian Anal. 14 (3) 727 - 751, September 2019.


Published: September 2019
First available in Project Euclid: 11 June 2019

zbMATH: 1421.62048
MathSciNet: MR3960768
Digital Object Identifier: 10.1214/18-BA1125

Primary: 62F15 , 62M10
Secondary: 62G99

Keywords: Bayesian semiparametric inference , Dirichlet process mixture , heterogeneous transition matrices , interest rates

Vol.14 • No. 3 • September 2019
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