Semiparametric Multivariate and Multiple Change-Point Modeling

Stefano Peluso, Siddhartha Chib, Antonietta Mira

Risultato della ricerca: Contributo in rivistaArticolo in rivista


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.
Lingua originaleEnglish
pagine (da-a)727-751
Numero di pagine25
RivistaBayesian Analysis
Stato di pubblicazionePubblicato - 2018


  • Bayesian Semiparametric Inference
  • Dirichlet Process Mixture
  • Heterogeneous Transition Matrices
  • Interest Rates

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