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Kriging has its roots in the mining industry and solves the following problem: Consider the random ÿeld Z(x), where x represents a geographic location and Z(x) the depth of an ore ÿeld at x. Given observations Z(x 1 ); : : : ; Z(x n ), one might want to predict Z(x 0 ) at some speciÿed location x 0 . The kriging solution, when the mean function m(x) = E[Z(x)] is a constant or known, is to model the covariance function K(x; y) = cov(Z(x); Z(y)) and then to take the BLUE estimate as if the estimated covariance function were correct. It is not hard to think of a wide variety of applications of this problem in many ÿelds beyond mining. The author states that this book is not meant to be a comprehensive introduction to the practice of kriging. He recommends Cressie (1993) for that purpose. Rather, the book is a very personal, and sometimes confrontational, account of the author's own research into the behavior of kriging estimates under inÿll or ÿxed domain asymptotics. Under the usual form of extended domain asymptotics, as in time series for example, additional observations are taken in a way to increase the range of x; in inÿll asymptotics the additional observations are taken so tha

Who reads Interpolation of spatial data: some theory for kriging: Michael L. Stein; Springer, New York, 1999, 247+xvii pages, ISBN 0-387-98629-4?

Author

Ted Chang

Publisher

Elsevier Science; Elsevier ; Elsevier BV (ISSN 0378-3758)

Published

2001

Language

EN

Field

Mathematics (Physical Sciences)