- Bayesian Anal.
- Advance publication (2018), 25 pages.
Semiparametric Multivariate and Multiple Change-Point Modeling
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.
Bayesian Anal., Advance publication (2018), 25 pages.
First available in Project Euclid: 5 October 2018
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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
- Supplementary Material to Semiparametric Multivariate and Multiple Change-Point Modeling.