Adaptively varying-coefficient spatiotemporal models

TitleAdaptively varying-coefficient spatiotemporal models
Publication TypeJournal Article
Year of Publication2009
AuthorsZudi, L, Steinskog, DJ, Tjøstheim, D, Yao, Q
JournalJournal of the Royal Statistical Society: Series B (Statistical Methodology)
Date Published06/2009
PublisherThe Royal Statistical Society and Blackwell Publishing Ltd
KeywordsKernel smoothing, Local linear regression, Nugget effect, Spatial smoothing, Unilateral order, β-mixing

Summary. We propose an adaptive varying-coefficient spatiotemporal model for data that are observed irregularly over space and regularly in time. The model is capable of catching possible non-linearity (both in space and in time) and non-stationarity (in space) by allowing the auto-regressive coefficients to vary with both spatial location and an unknown index variable. We suggest a two-step procedure to estimate both the coefficient functions and the index variable, which is readily implemented and can be computed even for large spatiotemporal data sets. Our theoretical results indicate that, in the presence of the so-called nugget effect, the errors in the estimation may be reduced via the spatial smoothing—the second step in the estimation procedure proposed. The simulation results reinforce this finding. As an illustration, we apply the methodology to a data set of sea level pressure in the North Sea.

Refereed DesignationUnknown
Author Address