My research focuses on statistical models and methods for spatial and
spatial-temporal processes. In particular, I am interested in the nature of
the spatial-temporal interactions implied by these models and on developing
statistical methods for assessing these interactions.
Ralph and Mary Otis Isham
Department of Statistics and the College
My main motivation for studying spatial-temporal processes is to describe
variations in the physical environment. In recent years, most of my efforts
in this direction relate to problems in climate and weather. Two
topics of current interest are methods for combining output from
deterministic climate models with observational data to produce realistic
spatially and temporally resolved simulations of future temperature and
precipitation fields, and ways of estimating temperature extremes that take
proper account of seasonality, long-term trends and spatial structure.
Climate datasets (either computer-generated or observational) are generally
quite large, leading to two major thrusts of my research. First, when one
has lots of spatial-temporal data for the natural environment, it is
often apparent that the process is nonstationary in space, time, or both.
Thus, the development of nonstationary models plays a central role in my
current research. Second, when one has large datasets with complex
computational issues are critical as they relate to evaluating likelihoods
and running simulations. Much of my recent research has considered bringing
to bear modern tools from numerical linear algebra to these computational