Semiparametric Multivariate and Multiple Change-Point Modeling
Semiparametric Multivariate and Multiple Change-Point Modeling is a scholarly work, published in 2019 in ''Bayesian Analysis''. The main subjects of the publication include multivariate statistics, regularization, econometrics, mixture model, Markov chain Monte Carlo, point process, risk, Bayesian inference, Dirichlet distribution, statistics, Gibbs sampling, applied mathematics, mathematics, Dirichlet process, computer science, and Bayesian probability. The authors 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.