Bayesian Analysis

Semiparametric Multivariate and Multiple Change-Point Modeling

Stefano Peluso, Siddhartha Chib, and Antonietta Mira

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Abstract

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

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

Dates
First available in Project Euclid: 5 October 2018

Permanent link to this document
https://projecteuclid.org/euclid.ba/1538704892

Digital Object Identifier
doi:10.1214/18-BA1125

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

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

Rights
Creative Commons Attribution 4.0 International License.

Citation

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. https://projecteuclid.org/euclid.ba/1538704892


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