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Interpolation of Spatial Data Some Theory for Kriging

by Michael L. Stein

  • ISBN: 9780387986296
  • ISBN10: 0387986294

Interpolation of Spatial Data Some Theory for Kriging

by Michael L. Stein

  • List Price: $169.00
  • Binding: Hardcover
  • Publisher: Springer Verlag
  • Publish date: 06/01/1999
  • ISBN: 9780387986296
  • ISBN10: 0387986294
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Description: 1 Linear Prediction.- 1.1 Introduction.- 1.2 Best linear prediction.- Exercises.- 1.3 Hilbert spaces and prediction.- Exercises.- 1.4 An example of a poor BLP.- Exercises.- 1.5 Best linear unbiased prediction.- Exercises.- 1.6 Some recurring themes.- The Matrn model.- BLPs and BLUPs.- Inference for differentiable random fields.- Nested models are not tenable.- 1.7 Summary of practical suggestions.- 2 Properties of Random Fields.- 2.1 Preliminaries.- Stationarity.- Isotropy.- Exercise.- 2.2 The turning bands method.- Exercise.- 2.3 Elementary properties of autocovariance functions.- Exercise.- 2.4 Mean square continuity and differentiability.- Exercises.- 2.5 Spectral methods.- Spectral representation of a random field.- Bochner''s Theorem.- Exercises.- 2.6 Two corresponding Hilbert spaces.- An application to mean square differentiability.- Exercises.- 2.7 Examples of spectral densities on 112.- Rational spectral densities.- Principal irregular term.- Gaussian model.- Triangular autocovariance functions.- Matrn class.- Exercises.- 2.8 Abelian and Tauberian theorems.- Exercises.- 2.9 Random fields with nonintegrable spectral densities.- Intrinsic random functions.- Semivariograms.- Generalized random fields.- Exercises.- 2.10 Isotropic autocovariance functions.- Characterization.- Lower bound on isotropic autocorrelation functions.- Inversion formula.- Smoothness properties.- Matrn class.- Spherical model.- Exercises.- 2.11 Tensor product autocovariances.- Exercises.- 3 Asymptotic Properties of Linear Predictors.- 3.1 Introduction.- 3.2 Finite sample results.- Exercise.- 3.3 The role of asymptotics.- 3.4 Behavior of prediction errors in the frequency domain.- Some examples.- Relationship to filtering theory.- Exercises.- 3.5 Prediction with the wrong spectral density.- Examples of interpolation.- An example with a triangular autocovariance function.- More criticism of Gaussian autocovariance functions.- Examples of extrapolation.- Pseudo-BLPs with spectral densities misspecified at high frequencies.- Exercises.- 3.6 Theoretical comparison of extrapolation and ointerpolation.- An interpolation problem.- An extrapolation problem.- Asymptotics for BLPs.- Inefficiency of pseudo-BLPs with misspecified high frequency behavior.- Presumed mses for pseudo-BLPs with misspecified high frequency behavior.- Pseudo-BLPs with correctly specified high frequency behavior.- Exercises.- 3.7 Measurement errors.- Some asymptotic theory.- Exercises.- 3.8 Observations on an infinite lattice.- Characterizing the BLP.- Bound on fraction of mse of BLP attributable to a set of frequencies.- Asymptotic optimality of pseudo-BLPs.- Rates of convergence to optimality.- Pseudo-BLPs with a misspecified mean function.- Exercises.- 4 Equivalence of Gaussian Measures and Prediction.- 4.1 Introduction.- 4.2 Equivalence and orthogonality of Gaussian measures.- Conditions for orthogonality.- Gaussian measures are equivalent or orthogonal.- Determining equivalence or orthogonality for periodic random fields.- Determining equivalence or orthogonality for nonperiodic random fields.- Measurement errors and equivalence and orthogonality.- Proof of Theorem 1.- Exercises.- 4.3 Applications of equivalence of Gaussian measures to linear prediction.- Asymptotically optimal pseudo-BLPs.- Observations not part of a sequence.- A theorem of Blackwell and Dubins.- Weaker conditions for asymptotic optimality of pseudo-BLPs.- Rates of convergence to asymptotic optimality.- Asymptotic optimality of BLUPs.- Exercises.- 4.4 Jeffreys''s law.- A Bayesian version.- Exercises.- 5 Integration of Random Fields.- 5.1 Introduction.- 5.2 Asymptotic properties of simple average.- Results for sufficiently smooth random fields.- Results for sufficiently rough random fields.- Exercises.- 5.3 Observations on an infinite lattice.- Asymptotic mse of BLP.- Asymptotic optimality of simple average.- Exercises.- 5.4 Improving on the sample mean.- Approximating$$int_0^1 {exp } (ivt)dt$$.- Approximating$$int_{{{[0,1]}^d}} {exp (i{omega ^T}x)} dx$$in more than one dimension.- Asymptotic properties of modified predictors.- Are centered systematic samples good designs'.- Exercises.- 5.5 Numerical results.- Exercises.- 6 Predicting With Estimated Parameters.- 6.1 Introduction.- 6.2 Microergodicity and equivalence and orthogonality of Gaussian measures.- Observations with measurement error.- Exercises.- 6.3 Is statistical inference for differentiable processes possible'.- An example where it is possible.- Exercises.- 6.4 Likelihood Methods.- Restricted maximum likelihood estimation.- Gaussian assumption.- Computational issues.- Some asymptotic theory.- Exercises.- 6.5 Matrn model.- Exercise.- 6.6 A numerical study of the Fisher information matrix under the Matrn model.- No measurement error and'unknown.- No measurement error and'known.- Observations with measurement error.- Conclusions.- Exercises.- 6.7 Maximum likelihood estimation for a periodic version of the Matrn model.- Discrete Fourier transforms.- Periodic case.- Asymptotic results.- Exercises.- 6.8 Predicting with estimated parameters.- Jeffreys''s law revisited.- Numerical results.- Some issues regarding asymptotic optimality.- Exercises.- 6.9 An instructive example of plug-in prediction.- Behavior of plug-in predictions.- Cross-validation.- Application of Matrn model.- Conclusions.- Exercises.- 6.10 Bayesian approach.- Application to simulated data.- Exercises.- A Multivariate Normal Distributions.- B Symbols.- References.
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