Bayesian Analysis

Semiparametric Multivariate and Multiple Change-Point Modeling

Stefano Peluso, Siddhartha Chib, and Antonietta Mira

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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.

Article information

Bayesian Anal., Advance publication (2018), 25 pages.

First available in Project Euclid: 5 October 2018

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Digital Object Identifier

Primary: 62F15: Bayesian inference 62M10: Time series, auto-correlation, regression, etc. [See also 91B84]
Secondary: 62G99: None of the above, but in this section

Bayesian semiparametric inference Dirichlet process mixture heterogeneous transition matrices interest rates

Creative Commons Attribution 4.0 International License.


Peluso, Stefano; Chib, Siddhartha; Mira, Antonietta. Semiparametric Multivariate and Multiple Change-Point Modeling. Bayesian Anal., advance publication, 5 October 2018. doi:10.1214/18-BA1125.

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