Interpolation of Spatial Data

Some Theory for Kriging

  • Michael L. Stein
Part of the Springer Series in Statistics book series (SSS)

Table of contents

  1. Front Matter
    Pages i-xvii
  2. Michael L. Stein
    Pages 1-14
  3. Michael L. Stein
    Pages 15-56
  4. Michael L. Stein
    Pages 57-108
  5. Michael L. Stein
    Pages 109-143
  6. Michael L. Stein
    Pages 144-159
  7. Michael L. Stein
    Pages 160-228
  8. Back Matter
    Pages 229-249

About this book

Introduction

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.

Keywords

Kriging Likelihood Normal distribution STATISTICA Spatial Data Spatial Statistics Variance digital elevation model geographic data linear optimization

Authors and affiliations

  • Michael L. Stein
    • 1
  1. 1.Department of StatisticsUniversity of ChicagoChicagoUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4612-1494-6
  • Copyright Information Springer-Verlag New York, Inc. 1999
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4612-7166-6
  • Online ISBN 978-1-4612-1494-6
  • Series Print ISSN 0172-7397
  • About this book