Hao Wang
Department of Statistical Science
Duke University
Bayesian Multivariate Modelling: Graphical Models and Time Series
Modelling and inference with higher-dimensional variables, including studies
in multivariate time series analysis, raise challenges to our ability to
"scale-up" statistical approaches that involve both modelling and
computational issues. Modelling issues relate to the interest in parsimony
of parametrization and control over proliferation of parameters;
computational issues relate to the basic challenges to the efficiency of
statistical computation (simulation and optimization) with increasingly
high-dimensional and structured models. I am interested in these questions
and in Bayesian approaches inducing relevant sparsity and structure into
parameter spaces, with a particular focus on time series and dynamic
modelling.
I will outline some of my research on integrating statistical graphical
modelling ideas into
multi- and matrix-variate dynamic models of various kinds. This will include
the development and application of dynamic graphical models for multivariate
financial time series and matrix-variate econometric examples, and dynamic
multivariate regression modelling with simultaneous selection of variables
and graphical-model structured covariance matrices. The talk will highlight
a blend of Bayesian modelling and computational developments as well as
examples, and mention potential extensions, additional areas for
application, and a number of new and open research directions.
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