Department of Statistics, The Ohio State University
Statistics and Biostatistics Colloquium Series

In an effort to bridge the gap between theory and practice, we explore the impacts of spatial correlation on parameter estimation and model selection. With regards to parameter estimation, we investigate the influence of the strength of spatial dependence on maximum likelihood and restricted maximum likelihood estimates of covariance parameters. Spatial correlation can also impact model selection, but is often ignored. We show that using AIC for a geostatistical model is superior to the more traditional approach of ignoring spatial correlation in the selection of explanatory variables. Much of this work includes consideration of the effects of different sampling designs and sampling intensities. This is joint work with several research groups including Richard Davis, Alix Gitelman, Kathryn Irvine, Andrew Merton, and Sandra Thompson.

Meet the speaker in Room 212 Cockins Hall at 4:30 p.m. Refreshments will be served.