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Interpolation of Spatial Data

Some Theory for Kriging

  • Book
  • © 1999

Overview

Authors:
  1. Michael L. Stein

    1. Department of Statistics, University of Chicago, Chicago, USA

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Part of the book series: Springer Series in Statistics (SSS)

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Table of contents (6 chapters)

  1. Front Matter

    Pages i-xvii
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  2. Linear Prediction

    • Michael L. Stein
    Pages 1-14

  3. Properties of Random Fields

    • Michael L. Stein
    Pages 15-56

  4. Asymptotic Properties of Linear Predictors

    • Michael L. Stein
    Pages 57-108

  5. Equivalence of Gaussian Measures and Prediction

    • Michael L. Stein
    Pages 109-143

  6. Integration of Random Fields

    • Michael L. Stein
    Pages 144-159

  7. Predicting With Estimated Parameters

    • Michael L. Stein
    Pages 160-228

  8. Back Matter

    Pages 229-249
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Keywords

About this book

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.

Reviews

 

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."

 

Authors and Affiliations

  • Department of Statistics, University of Chicago, Chicago, USA

    Michael L. Stein

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