Seminar Announcement
| Hierarchical spatial modeling of additive and dominance genetic variance for large spatial trial datasets |
Dr. Sudipto Banerjee, PhD, Division of Biostatistics, School of Public Health, University of Minnesota.
Monday, February 23, 2009
4:00 pm 223 Weber
| ABSTRACT |
With accessibility to geo-coded locations where scientific data are collected hrough Geographical Information Systems (GIS), investigators
in genetic field trials are increasingly turning to spatial process
models for modelling associations and relationships over space. Over the
last decade hierarchical models implemented through Markov Chain Monte
Carlo (MCMC) methods have become especially popular for genetic models,
given their flexibility and power to estimate models (and hence address
scientific hypothesis) that would be infeasible otherwise. However,
introducing spatial correlations often entails expensive matrix
decompositions whose computational complexity increases exponentially
with the number of spatial locations, rendering them infeasible for
large trials.
In this talk we primarily focus upon the use of a predictive process
derived from the original spatial process that projects process
realizations to a lower-dimensional subspace thereby reducing the
computational burden. This approach can be looked upon as a
process-based approach to reduced-rank methods for "kriging". We discuss
attractive theoretical properties of this predictive process as well as
its greater modeling flexibility compared to existing methods. In
particular, we show how the predictive process seamlessly adapts to
settings with non-stationary and multivariate processes. We also discuss
some pitfalls of this and other reduced-rank methods and offer remedies.
A computationally feasible template that encompasses these diverse
settings will be presented and illustrated. (Joint work with Andrew O.
Finley, Patrick Waldmann, and Tore Ericsson.)