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Spatial regression analysis using Eigenvector spatial filtering /

"Spatial Regression Analysis Using Eigenvector Spatial Filtering provides both theoretical foundations and guidance on practical implementation for the eigenvector spatial filtering (ESF) technique. ESF is a novel and powerful spatial statistical methodology that allows spatial scientists to ac...

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Bibliographic Details
Main Authors: Griffith, Daniel A (Author)
Group Author: Chun, Yongwan (writer of foreword.); Li, Bin (series editor.); Legendre, Pierre; Arbia, Giuseppe.
Published: Academic Press,
Publisher Address: London, United Kingdom :
Publication Dates: [2019]
Literature type: Book
Language: English
Series: Spatial econometrics and spatial statistics
Subjects:
Spatial analysis (Statistics)
Regression analysis.
Eigenvectors.
Summary: "Spatial Regression Analysis Using Eigenvector Spatial Filtering provides both theoretical foundations and guidance on practical implementation for the eigenvector spatial filtering (ESF) technique. ESF is a novel and powerful spatial statistical methodology that allows spatial scientists to account for spatial autocorrelation in georeferenced data analyses. With its flexible structure, ESF can be easily applied to generalized linear regression models. The book discusses ESF specifications for various intermediate-level topics, including spatially varying coefficients models, (non) linear mixed models, local spatial autocorrelation, and spatial interaction models. In addition, it provides a tutorial for ESF model specification and interfaces, including author developed, user-friendly software"--
Carrier Form: xxviii, 258 pages : illustrations (some color), forms ; 23 cm.
Bibliography: Includes bibliographical references and index.
ISBN: 9780128150436 (paperback) :
0128150432 (paperback)
Index Number: QA278
CLC: O212.1
Call Number: O212.1/G853

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