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US$ 153.49
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Condition: New. pp. 286.
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Spatial Regression Analysis Using Eigenvector Spatial Filtering
Published by Elsevier Science Publishing Co Inc, 2019
ISBN 10: 0128150432 ISBN 13: 9780128150436
Language: English
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
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Spatial Regression Analysis Using Eigenvector Spatial Filtering
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Spatial Regression Analysis Using Eigenvector Spatial Filtering
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Spatial Regression Analysis Using Eigenvector Spatial Filtering
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Spatial Regression Analysis Using Eigenvector Spatial Filtering
Published by Elsevier Science & Technology, Academic Press, 2019
ISBN 10: 0128150432 ISBN 13: 9780128150436
Language: English
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Add to basketTaschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Spatial Regression Analysis Using Eigenvector Spatial Filtering provides theoretical foundations and guides practical implementation of the Moran eigenvector spatial filtering (MESF) technique. MESF is a novel and powerful spatial statistical methodology that allows spatial scientists to account for spatial autocorrelation in their georeferenced data analyses. Its appeal is in its simplicity, yet its implementation drawbacks include serious complexities associated with constructing an eigenvector spatial filter. This book discusses MESF specifications for various intermediate-level topics, including spatially varying coefficients models, (non) linear mixed models, local spatial autocorrelation, space-time models, and spatial interaction models. Spatial Regression Analysis Using Eigenvector Spatial Filtering is accompanied by sample R codes and a Windows application with illustrative datasets so that readers can replicate the examples in the book and apply the methodology to their own application projects. It also includes a Foreword by Pierre Legendre. Englisch.
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US$ 158.08
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Add to basketPaperback. Condition: Brand New. 432 pages. 8.90x5.90x0.70 inches. In Stock. This item is printed on demand.
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Spatial Regression Analysis Using Eigenvector Spatial Filtering
Published by Elsevier Science & Technology|Academic Press, 2019
ISBN 10: 0128150432 ISBN 13: 9780128150436
Language: English
US$ 157.93
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Add to basketKartoniert / Broschiert. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Spatial Regression Analysis Using Eigenvector Spatial Filtering provides theoretical foundations and guides practical implementation of the Moran eigenvector spatial filtering (MESF) technique. MESF is a novel and powerful spatial statistical meth.
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Spatial Regression Analysis Using Eigenvector Spatial Filtering
Published by Elsevier Science & Technology, Academic Press, 2019
ISBN 10: 0128150432 ISBN 13: 9780128150436
Language: English
US$ 175.61
Convert currencyUS$ 73.12 shipping from Germany to U.S.A.Quantity: 2 available
Add to basketTaschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Spatial Regression Analysis Using Eigenvector Spatial Filtering provides theoretical foundations and guides practical implementation of the Moran eigenvector spatial filtering (MESF) technique. MESF is a novel and powerful spatial statistical methodology that allows spatial scientists to account for spatial autocorrelation in their georeferenced data analyses. Its appeal is in its simplicity, yet its implementation drawbacks include serious complexities associated with constructing an eigenvector spatial filter. This book discusses MESF specifications for various intermediate-level topics, including spatially varying coefficients models, (non) linear mixed models, local spatial autocorrelation, space-time models, and spatial interaction models. Spatial Regression Analysis Using Eigenvector Spatial Filtering is accompanied by sample R codes and a Windows application with illustrative datasets so that readers can replicate the examples in the book and apply the methodology to their own application projects. It also includes a Foreword by Pierre Legendre.
