1. Meet the professor Friday, January 23 at SFU 4:30 Beer and snacks reception

2. Spatial Covariance NRCSE

3. Valid covariance functions Bochner’s theorem: The class of covariance functions is the class of positive definite functions C: Why?

4. By the spectral representation any isotropic continuous correlation on R d is of the form By isotropy, the expectation depends only on the distribution G of. Let Y be uniform on the unit sphere. Then Spectral representation

5. Isotropic correlation J v (u) is a Bessel function of the first kind and order v. Hence and in the case d=2 (Hankel transform)

6. The Bessel function J 0 –0.403

7. The exponential correlation A commonly used correlation function is  (v) = e –v/ . Corresponds to a Gaussian process with continuous but not differentiable sample paths. More generally,  (v) = c(v=0) + (1-c)e –v/  has a nugget c, corresponding to measurement error and spatial correlation at small distances. All isotropic correlations are a mixture of a nugget and a continuous isotropic correlation.

8. The squared exponential Usingyields corresponding to an underlying Gaussian field with analytic paths. This is sometimes called the Gaussian covariance, for no really good reason. A generalization is the power(ed) exponential correlation function,

9. The spherical Corresponding variogram nugget sillrange

11. The Matérn class where is a modified Bessel function of the third kind and order . It corresponds to a spatial field with [  –1] continuous derivatives  =1/2 is exponential;  =1 is Whittle’s spatial correlation; yields squared exponential.

13. Some other covariance/variogram families NameCovarianceVariogram Wave Rational quadratic LinearNone Power lawNone

14. Recall Method of moments: square of all pairwise differences, smoothed over lag bins Problems: Not necessarily a valid variogram Not very robust Estimation of variograms

15. A robust empirical variogram estimator (Z(x)-Z(y)) 2 is chi-squared for Gaussian data Fourth root is variance stabilizing Cressie and Hawkins:

16. Least squares Minimize Alternatives: fourth root transformation weighting by 1/  2 generalized least squares

17. Maximum likelihood Z~N n ( ,  )  =  [  (s i -s j ;  )] =  V(  ) Maximize and  maximizes the profile likelihood

18. A peculiar ml fit

19. Some more fits

20. All together now...

21. Asymptotics Increasing domain asymptotics: let region of interest grow. Station density stays the same Bad estimation at short distances, but effectively independent blocks far apart Infill asymptotics: let station density grow, keeping region fixed. Good estimates at short distances. No effectively independent blocks, so technically trickier

22. Stein’s result Covariance functions C 0 and C 1 are compatible if their Gaussian measures are mutually absolutely continuous. Sample at {s i, i=1,...,n}, predict at s (limit point of sampling points). Let e i (n) be kriging prediction error at s for C i, and V 0 the variance under C 0 of some random variable. If lim n V 0 (e 0 (n))=0, then

23. The Fourier transform

24. Properties of Fourier transforms Convolution Scaling Translation

25. Parceval’s theorem Relates space integration to frequency integration. Decomposes variability.

26. Aliasing Observe field at lattice of spacing . Since the frequencies  and  ’=  +2  m/  are aliases of each other, and indistinguishable. The highest distinguishable frequency is , the Nyquist frequency.

27. Illustration of aliasing Aliasing applet

28. Spectral representation Stationary processes Spectral process Y has stationary increments If F has a density f, it is called the spectral density.

29. Estimating the spectrum For process observed on nxn grid, estimate spectrum by periodogram Equivalent to DFT of sample covariance

30. Properties of the periodogram Periodogram values at Fourier frequencies (j,k)  are uncorrelated asymptotically unbiased not consistent To get a consistent estimate of the spectrum, smooth over nearby frequencies

31. Some common isotropic spectra Squared exponential Matérn

32. A simulated process

33. Thetford canopy heights 39-year thinned commercial plantation of Scots pine in Thetford Forest, UK Density 1000 trees/ha 36m x 120m area surveyed for crown height Focus on 32 x 32 subset

34. Spectrum of canopy heights

35. Whittle likelihood Approximation to Gaussian likelihood using periodogram: where the sum is over Fourier frequencies, avoiding 0, and f is the spectral density Takes O(N logN) operations to calculate instead of O(N 3 ).

36. Using non-gridded data Consider where Then Y is stationary with spectral density Viewing Y as a lattice process, it has spectral density

37. Estimation Let where J x is the grid square with center x and n x is the number of sites in the square. Define the tapered periodogram where. The Whittle likelihood is approximately

38. A simulated example

39. Estimated variogram