1. Space-time statistics STAT 518 Sp08

2. Space-time processes Separable covariance structure: Cov(Z(x,t),Z(y,s))=C 1 (x,y)C 2 (s,t) Nonseparable alternatives Temporally varying spatial covariances Fourier approach Completely monotone functions

3. SARMAP study Spatial correlation structure depends on hour of the day (non-separable):

4. A national health effects study

6. Trend model where V ik are covariates, such as population density, proximity to roads, local topography, etc. where the f j are smoothed versions of temporal singular vectors (EOFs) of the TxN data matrix. We will set  1 (s i ) =  0 (s i ) for now.

7. SVD computation

8. EOF 1

9. EOF 2

10. EOF 3

12. Kriging of  0

13. Kriging of  2

14. Quality of trend fits

15. Observed vs. predicted