For the following questions, answer T (true) or F (false). (a) For a Matérn covariance model, the larger ν (the smoothness parameter) is, the smoother the spatial data is over spac... For the following questions, answer T (true) or F (false). (a) For a Matérn covariance model, the larger ν (the smoothness parameter) is, the smoother the spatial data is over space. (b) When ν = 0.5, the Matérn covariance function is the same as the exponential covariance function. (c) If a certain spatial data set is assumed to be geometrically anisotropic, it is stationary. (d) When performing Kriging, kriging weights (i.e. weight value assigned to each observation) must be non-negative. (e) For a geometrically anisotropic random field, its variance does not change over its spatial domain. (f) The unit of spatial range is the same as the unit of the spatial distance. (g) Suppose we have spatial data, Z1, Z2, ..., Zn, observed from n locations, s1, s2, ..., sn, respectively. If there is no spatial dependence in this data set, predicted value for the spatial variable using Kriging method is equal to n1 (Z1 + · · · + Zn). (h) If we perform Kriging with a spatial regression model, Z(s) = β0 + β1X(s) + e(s) (with unknown parameters β0 and β1), it is ordinary Kriging. (i) A Gaussian random field with zero mean is always isotropic. (j) If we use Gaussian random field model for spatial data, its Kriging predictors also follow Gaussian distribution. Read more