Publication Date:
2012-04-17
Description:
In this paper, we extend the applicability of a previously proposed class of dynamic space-time models by enabling them to accommodate large datasets. We focus on the common setting where space is viewed as continuous but time is taken to be discrete. Scalability is achieved by using a low-rank predictive process to reduce the dimensionality of the data and ease the computational burden of estimating the spatio-temporal process of interest. The proposed models are illustrated using weather station data collected over the northeastern United States between 2000 and 2005. Here our interest is to use readily available predictors, association among measurements at a given station, as well as dependence across space and time to improve prediction for incomplete station records and locations where station data does not exist. Content Type Journal Article Category Original Article Pages 29-47 DOI 10.1007/s10109-011-0154-8 Authors Andrew O. Finley, Departments of Geography and Forestry, Michigan State University, East Lansing, MI, USA Sudipto Banerjee, Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA Alan E. Gelfand, Department of Statistical Science, Duke University, Durham, NC, USA Journal Journal of Geographical Systems Online ISSN 1435-5949 Print ISSN 1435-5930 Journal Volume Volume 14 Journal Issue Volume 14, Number 1
Print ISSN:
1435-5930
Electronic ISSN:
1435-5949
Topics:
Geography
