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Department of Statistics
Texas A&M University
STATISTICS COLLOQUIUM
DEPARTMENT OF STATISTICS
Texas A&M University
Alan E. Gelfand
Department of Statistics
University of Connecticut
MISALIGNED SPATIAL DATA
ABSTRACT: In attempting to establish relationships between spatial variables, one often encounters misaligned data layers. For example, the response variable may be observed on one areal grid and the explanatory variable on another. In some cases one variable is observed areally while the other is observed at point sources. In attempting to reconcile such misalignment, one finds a variety of ad hoc methods in the literature. We propose fully model-based approaches to develop such regressions. Such models are necessarily hierarchical and provide full inference, avoiding typically inappropriate asymptotics associated with likelihood-based approaches. We dicuss general approaches for handling the different types of misalignment and provide illustrative analyses.
| DATE: | Thursday, September 28, 2000 | |
| TIME: | 4:00 p.m.-5:00 p.m. | |
| PLACE: | Room 150, Blocker |
Refreshments will be served in the Blocker Building, Room 447, at 3:30 p.m.
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