Marco A. R. Ferreira
University of Missouri
Dynamic Multiscale Spatio-Temporal Models for Gaussian Areal Data
We introduce a new class of dynamic multiscale models for spatio-temporal processes
arising from Gaussian areal data. Specifically, we use nested geographical structures
to decompose the original process into multiscale coefficients which evolve through
time following state-space equations. Our approach naturally accommodates data
observed on irregular grids as well as heteroscedasticity. Moreover, we propose a
multiscale spatio-temporal clustering algorithm that facilitates estimation of the
nested geographical multiscale structure. In addition, we present a singular forward
filter backward sampler for efficient Bayesian estimation. Our multiscale spatio-temporal
methodology decomposes large data-analysis problems into many smaller components and
thus leads to scalable and highly efficient computational procedures. Finally,
we illustrate the utility and flexibility of our dynamic multiscale framework through
two spatio-temporal applications. The first example considers mortality ratios in the
state of Missouri whereas the second example examines agricultural production in
Espirito Santo State Brazil.
This is joint work S. Holan and A. Bertolde.