PLACE: 171 Durham

SPEAKER:
Jonathan Stroud
Department of Statistics, The University of Chicago

TITLE:
Dynamic Models for Spatio-Temporal Data

ABSTRACT:

In the first part of the talk, we develop a general framework for
spatio-temporal modeling.  At each time period, we write the spatial mean
function as a locally-weighted mixture of linear regressions.  To incorporate
temporal variation, we allow the regression coefficients to change over time.
The model is cast in a Gaussian state-space framework, which allows us to
incorporate nonstationary components such as temporal trends and seasonality,
and permits efficient implemention and full probabilistic inference for the
parameters, interpolations and forecasts. To illustrate the methodology, we
analyze a large dataset of Venezuelan rainfall levels.

In the second half of the talk, we consider the problem of ozone monitoring in
Mexico City.  The data consist of hourly observations of ozone, humidity, NOx,
and wind velocity from a network of 19 stations. The ozone exhibits strong
diurnal patterns and space-time interactions, due to photochemical and
transport processes.  We develop a seasonal state-space model that incorporates
wind flows, NOx and other predictor variables, and implement it using empirical
Bayes methods.
 
 
 
 

COFFEE: 10:30 a.m., 104 Snedecor