- Bayesian Anal.
- Volume 14, Number 3 (2019), 727-751.
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., Volume 14, Number 3 (2019), 727-751.
First available in Project Euclid: 11 June 2019
Permanent link to this document
Digital Object Identifier
Mathematical Reviews number (MathSciNet)
Zentralblatt MATH identifier
Peluso, Stefano; Chib, Siddhartha; Mira, Antonietta. Semiparametric Multivariate and Multiple Change-Point Modeling. Bayesian Anal. 14 (2019), no. 3, 727--751. doi:10.1214/18-BA1125. https://projecteuclid.org/euclid.ba/1560240025
- Supplementary Material to Semiparametric Multivariate and Multiple Change-Point Modeling.