Cantitate/Preț
Produs
Total produse
Vezi coșul
Interpolation of Spatial Data: Some Theory for Kriging de Michael L. Stein

Interpolation of Spatial Data: Some Theory for Kriging: Springer Series in Statistics

Autor Michael L. Stein
en Limba Engleză Hardback – 22 iun 1999
Prediction of a random field based on observations of the random field at some set of locations arises in mining, hydrology, atmospheric sciences, and geography. Kriging, a prediction scheme defined as any prediction scheme that minimizes mean squared prediction error among some class of predictors under a particular model for the field, is commonly used in all these areas of prediction. This book summarizes past work and describes new approaches to thinking about kriging.
Citește tot Restrânge

Toate formatele și edițiile

Toate formatele și edițiile Preț Express
Paperback (1) 88897 lei  6-8 săpt.
  Springer – 17 oct 2012 88897 lei  6-8 săpt.
Hardback (1) 89274 lei  6-8 săpt.
  Springer – 22 iun 1999 89274 lei  6-8 săpt.

Din seria Springer Series in Statistics

Preț: 89274 lei

Preț vechi: 108870 lei
-18% Nou

Puncte Express: 1339
Preț estimativ în valută:
17084€ • 17728$ • 14280£

Carte tipărită la comandă

Livrare economică 15-29 martie

 Adaugă în coș

Wish list
Gift list
Am citit!
Adaugă în listă
Preluare comenzi: 021 569.72.76

Specificații

ISBN-13: 9780387986296
ISBN-10: 0387986294
Pagini: 249
Ilustrații: XVII, 249 p.
Dimensiuni: 155 x 235 x 17 mm
Greutate: 0.49 kg
Ediția:1999
Editura: Springer
Colecția Springer
Seria Springer Series in Statistics

Locul publicării:New York, NY, United States

Public țintă

Research

Cuprins

1 Linear Prediction.- 1.1 Introduction.- 1.2 Best linear prediction.- 1.3 Hilbert spaces and prediction.- 1.4 An example of a poor BLP.- 1.5 Best linear unbiased prediction.- 1.6 Some recurring themes.- 1.7 Summary of practical suggestions.- 2 Properties of Random Fields.- 2.1 Preliminaries.- 2.2 The turning bands method.- 2.3 Elementary properties of autocovariance functions.- 2.4 Mean square continuity and differentiability.- 2.5 Spectral methods.- 2.6 Two corresponding Hilbert spaces.- 2.7 Examples of spectral densities on 112.- 2.8 Abelian and Tauberian theorems.- 2.9 Random fields with nonintegrable spectral densities.- 2.10 Isotropic autocovariance functions.- 2.11 Tensor product autocovariances.- 3 Asymptotic Properties of Linear Predictors.- 3.1 Introduction.- 3.2 Finite sample results.- 3.3 The role of asymptotics.- 3.4 Behavior of prediction errors in the frequency domain.- 3.5 Prediction with the wrong spectral density.- 3.6 Theoretical comparison of extrapolation and ointerpolation.- 3.7 Measurement errors.- 3.8 Observations on an infinite lattice.- 4 Equivalence of Gaussian Measures and Prediction.- 4.1 Introduction.- 4.2 Equivalence and orthogonality of Gaussian measures.- 4.3 Applications of equivalence of Gaussian measures to linear prediction.- 4.4 Jeffreys’s law.- 5 Integration of Random Fields.- 5.1 Introduction.- 5.2 Asymptotic properties of simple average.- 5.3 Observations on an infinite lattice.- 5.4 Improving on the sample mean.- 5.5 Numerical results.- 6 Predicting With Estimated Parameters.- 6.1 Introduction.- 6.2 Microergodicity and equivalence and orthogonality of Gaussian measures.- 6.3 Is statistical inference for differentiable processes possible?.- 6.4 Likelihood Methods.- 6.5 Matérn model.- 6.6 A numerical study of the Fisherinformation matrix under the Matérn model.- 6.7 Maximum likelihood estimation for a periodic version of the Matérn model.- 6.8 Predicting with estimated parameters.- 6.9 An instructive example of plug-in prediction.- 6.10 Bayesian approach.- A Multivariate Normal Distributions.- B Symbols.- References.

Recenzii

 
From a review:
GEODERMA
"the book is written with great care and dedication. Soil geostatisticians that are not easily scared off by mathematics will find this book to be a rich source of inspiration for many years to come."