Collecting Spatial Data: Optimum Design of Experiments for Random Fields

Müller, Werner G.

ISBN 10: 3540311742 / ISBN 13: 9783540311744

Verlag: Springer, 2007

Beschreibung

Like New.

Bestandsnummer des Verkäufers ERICA70435403117426

Bibliografische Details

• Titel: Collecting Spatial Data: Optimum Design of Experiments for Random Fields
• Verlag: Springer
• Veröffentlichungsdatum: 2007
• Sprache: Englisch
• ISBN-10: 3540311742
• ISBN-13: 9783540311744
• Bindung: Hardcover
• Ausgabe: 3. Auflage
• Zustand: Like New
• Buchtyp: book

Über diesen Titel

Inhaltsangabe

The book is concerned with the statistical theory for locating spatial sensors. It bridges the gap between spatial statistics and optimum design theory. After introductions to those two fields the topics of exploratory designs and designs for spatial trend and variogram estimation are treated. Special attention is devoted to describing new methodologies to cope with the problem of correlated observations.

Von der hinteren Coverseite

The book is concerned with the statistical theory for locating spatial sensors. It bridges the gap between spatial statistics and optimum design theory. After introductions to those two fields the topics of exploratory designs and designs for spatial trend and variogram estimation are treated. Special attention is devoted to describing new methodologies to cope with the problem of correlated observations. A great number of relevant references are collected and put into a common perspective. The theoretical investigations are accompanied by a practical example, the redesign of an Upper-Austrian air pollution monitoring network. A reader should be able to find respective theory and recommendations on how to efficiently plan a specific purpose spatial monitoring network. The third edition takes into account the rapid development in the area of spatial statistics by including new relevant research and references. The revised edition contains additional material on design for detecting spatial dependence and for estimating parametrized covariance functions.