Stein, M.L. (1999). Interpolation of Spatial Data: Some Theory of Kriging. New York: Springer. Thomas, A., Best, N., Arnold, R. and Spiegelhalter, D. (2002). The GeoBUGS User Manual. (http://www.mrc-bsu.cam.ac.uk/bugs).  Home/Search Document Not in Database Summary Related Articles Check |
This paper is cited in the following contexts:
A Chronology of Interpolation: From Ancient Astronomy to Modern.. - Meijering (2002) (8 citations) (Correct)
....Matheron [310] in honor of D. G. Krige [311] a South African mining engineer who developed empirical methods for determining true ore grade distributions from distributions based on sampled ore grades [312] 313] Kriging has been studied in the context of geostatistics [312] 314] [315], cartography [316] and meteorology [317] and is closely related to interpolation by thin plate splines or radial basis functions [318] 319] In medical imaging, the technique seems to have been applied first by Stytz and Parrot [320] 321] More recent studies related to kriging in signal and ....
M. L. Stein, Interpolation of Spatial Data: Some Theory for Kriging. Berlin, Germany: Springer-Verlag, 1999.
Bayesian Analysis of Computer Code Outputs - Kennedy, O'Hagan, Higgins (2001) (Correct)
....will often demand much higher dimensional input spaces. The problem of the choice of x i s is one of experimental design. An important review of DACE work is Sacks et al. (1989) and for more recent work on computer experiment design see Bates et al. (1996) A good reference for geostatistics is Stein (1999). 1.3 SACCO The main focus in DACE was to predict the output (x) of the computer code at some untried input con. guration. The result is a statistical approximation to the code, that agrees with the observed outputs at the design inputs but interpolates smoothly between them to allow prediction ....
Stein, M. L. (1999) Interpolation of Spatial Data: Some Theory for Kriging. New York: Springer-Verlag.
Modeling and Testing for Non-Stationarity of Spatial Processes - Fuentes (Correct)
....ratio of the variance 2 i (also called the sill parameter) and the range ( 1 i ) to the 2 th i power, i = 2 i 2 i : The parameter i changes with location. The smoothness of a random eld, the parameter i in the Mat ern class, plays a critical role in interpolation problems (Stein, 1999). A number of commonly used models for the covariance structure, including spherical, exponential and Gaussian structures assume that the smoothness parameter is known a priori. The rst step in our analysis will be to estimate this parameter from the data, instead of considering it known. We ....
Stein, M. L. (1999). Interpolation of Spatial Data: some theory for kriging. Springer-Verlag, New York.
Variance Estimation in Spatial Regression Using a Nonparametric.. - Kim, Boos (2001) (1 citation) (Correct)
....h ae K 2 1=2 h ae 7 where oe is the scale parameter, the shape parameter, and ae measures how quickly the correlation of the random field decays with distance. The function Gamma( Delta) is the gamma function and K is the modified Bessel function of the third kind of order (Stein 1999). When = 1 2 , the model becomes the exponential model, and the limit 1 corresponds to a Gaussian model. The underlying true variogram is rarely known, and selection of a variogram model is quite arbitrary in practice. The Mat ern class appears to be the best choice of the present ....
Stein, M. (1999), Interpolation of Spatial Data: Some Theory for Kriging, Springer.
Geoadditive Models - Kammann, Wand (2000) (3 citations) (Correct)
....(y b 0 b T 1 x 0 ) 6) 7 where C = covfS(x i ) S(x j )g] 16i;j6n and c T 0 = covfS(x 0 ) S(x i )g] 16i6n : The practical implementation of (6) requires a parsimonious model for the interpoint covariances covfS(x) S(x 0 )g , x; x 0 2 R 2 . Following the recommendations of Stein (1999) we use covfS(x) S(x 0 )g = C (kx x 0 k) 7) where kvk = p v T v and C is member of the Matern family of covariance functions. It should be pointed out that (7) corresponds to S being isotropic, which we view as a reasonable working assumption for the application at hand. The most ....
...., where 2 x = VarfS(x)g is the variance of the process, is the range parameter and controls the distance at which covariances are effectively zero, and controls the smoothness of the resulting surface estimate. The full formulation of C is in terms of modified Bessel functions (e.g. Stein, 1999, p. 31) but the special case = 3=2 corresponds to C (r) 2 x (1 jrj= e jrj= 8) Indeed, in our analysis we work only with this sub family of the Matern covariance functions. We chose (8) because it is the simplest member of the Matern family that results in differentiable ....
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Stein, M.L. (1999). Interpolation of Spatial Data: Some Theory for Kriging, New York: Springer.
Spectral Methods for Nonstationary Spatial Processes - Fuentes (Correct)
....OE i are constant over the 15 domain. This is a consequence of the fact that low frequency behavior of the spectrum (small values of k k) or the higher order correlations, should have little effect on interpolation. It is possible to provide a quantitative theory supporting this point of view (Stein, 1999a) Consider f 0i to be the true spectral density of the process Z i and f 1i an incorrect one. If f 1i ( f 0i ( 1 as j j 1, then all linear predictors under the incorrect f 1i are uniformly asymptotically correct. Thus, if the goal is spatial interpolation, it is preferable to work on the spectral domain ....
Stein, M. L. (1999b). Interpolation of Spatial Data: some theory for kriging. Springer-Verlag, New York.
Interpolation Of Nonstationary Environmental Processes - Fuentes (Correct)
....i and OE i are constant over the domain. This is a consequence of the fact that low frequency behavior of the spectrum (small values of j j) or the higher order correlations, should have little effect on interpolation. It is possible to provide a quantitative theory supporting this point of view (Stein, 1999a, 1999b) Consider f 0i to be the true spectral density of the process Z i and f 1i an incorrect one. If f 1i ( f 0i ( 1 as j j 1, then all linear predictors under the incorrect f 1i are uniformly asymptotically correct. Thus, if the goal is spatial interpolation, it is preferable to work on the spectral domain ....
Stein, M. L. (1999b). Interpolation of Spatial Data: some theory for kriging. SpringerVerlag, New York.
Variance Estimation in Spatial Regression Using a Nonparametric.. - Kim, Boos (2001) (1 citation) (Correct)
....function: C(h) oe 2 Gamma1 Gamma( 2 1=2 h ae K 2 1=2 h ae where oe is the scale parameter, ae the range parameter, and is the shape parameter. The function Gamma( Delta) is the gamma function and K is the modified Bessel function of the third kind of order (Stein 1999). When = 1 2 , the model becomes the exponential model, and the limit 1 corresponds to a Gaussian model. The underlying true variogram is rarely known, and selection of a variogram model is quite arbitrary in practice. The Mat ern class appears to be the best choice of the present parametric ....
Stein, M. (1999), Interpolation of Spatial Data: Some Theory for Kriging, Springer.
Design and Analysis of Robust Total Joint.. - Chang, Williams.. (Correct)
....sampling area for the subsequent stage. Predictor of Response. We used an empirical best linear unbiased predictor (EBLUP) of , denoted by . The description of is given in Chang et a1. 1999) and is computed using the software GaSP 1 (Gaussian Stochastic Process) See Sacks, et al. 1989) and Stein (1999) for additional details regarding best linear unbiased prediction. Y x d x e , g x e ( x d L x d ( Y x d x e , g x e ( x e d = h x d ( 0 = h x d ( 0 h x d ( Y x d x e , Y x d x e , Y x d x e , L x d ( Y x d x e , L x d ( Y ....
Stein M.L., 1999, "Interpolation of Spatial Data: Some Theory for Kriging," Springer-Verlag, New York.
On Geodetic Distance Computations in Spatial Modelling - Banerjee (Correct)
No context found.
Stein, M.L. (1999). Interpolation of Spatial Data: Some Theory of Kriging. New York: Springer. Thomas, A., Best, N., Arnold, R. and Spiegelhalter, D. (2002). The GeoBUGS User Manual. (http://www.mrc-bsu.cam.ac.uk/bugs).
Frailty modeling for spatially correlated survival data.. - Banerjee, Wall, Carlin (2003) (Correct)
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STEIN,M.L.(1999). Interpolation of Spatial Data: Some Theory for Kriging.New York: Springer.
Choix D'une Covariance Pour La Prdiction Par Krigeage.. - Chronologiques.. (Correct)
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M. L. Stein. Interpolation of Spatial Data: Some Theory for Kriging. Springer, New York, 1999.
Gaussian Processes for Machine Learning - Seeger (2004) (Correct)
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M. Stein. Interpolation of Spatial Data: Some Theory for Kriging. Springer, 1999.
Bayesian Gaussian Process Models: PAC-Bayesian Generalisation.. - Seeger (2003) (3 citations) (Correct)
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M. Stein. Interpolation of Spatial Data: Some Theory for Kriging. Springer, 1999.
Nonstationary Covariance Functions for Gaussian Process.. - Paciorek, Schervish (2004) (Correct)
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M.L. Stein. Interpolation of Spatial Data : Some Theory for Kriging. Springer, N.Y., 1999.
Nonstationary Covariance Functions for Gaussian Process.. - Paciorek, Schervish (2003) (Correct)
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M.L. Stein. Interpolation of Spatial Data : Some Theory for Kriging. Springer, N.Y., 1999.
Slice Gibbs Sampling for Simulation Based - Fitting Of Spatial (Correct)
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Stein, M.L. (1999b) Interpolation of Spatial Data: Some Theory for Kriging. Springer-Verlag, New York.
Extrapolating and Interpolating Spatial Patterns - van Lieshout, Baddeley (2001) (1 citation) (Correct)
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M. Stein. Interpolation of spatial data: some theory for kriging. Springer Verlag, New York, 1999.
Space and Space-Time Modeling Using Process Convolutions - Higdon (Correct)
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Stein, M. (1999). Interpolation of Spatial Data: Some Theory for Kriging . New York: Springer-Verlag.
Spectral Analysis with Spatial Periodogram and Data Tapers - Kim, Fuentes (2000) (Correct)
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Biometrika, 69, 95-105. Stein, M. L. (1999). Interpolation of Spatial Data: some theory for kriging. Springer-Verlag, New York.
On the Change of Support Problem for Spatio-Temporal Data - Gelfand, Zhu, Carlin (2000) (2 citations) (Correct)
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Stein, M.L. (1999). Interpolation of Spatial Data: Some Theory for Kriging. New York: Springer-Verlag.
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