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Spatial regression analysis using Ei...

Griffith, Daniel.

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Spatial regression analysis using Eigenvector spatial filtering /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Spatial regression analysis using Eigenvector spatial filtering // Daniel A. Griffith, Yongwan Chun, Bin Li ; foreword by Pierre Legendre ; series editor Giuseppe Arbia.
Author:	Griffith, Daniel.
other author:	Chun, Yongwan.
Published:	London, UK ;Academic Press, : c2019.,
Description:	xxviii, 258 p. :ill. (some col.) ;23 cm.
Subject:	Spatial analysis (Statistics) -
ISBN:	9780128150436

Spatial regression analysis using Eigenvector spatial filtering /
Griffith, Daniel.

Spatial regression analysis using Eigenvector spatial filtering /Daniel A. Griffith, Yongwan Chun, Bin Li ; foreword by Pierre Legendre ; series editor Giuseppe Arbia. - London, UK ;Academic Press,c2019. - xxviii, 258 p. :ill. (some col.) ;23 cm. - Spatial econometrics and spatial statistics. - Spatial econometrics and spatial statistics..

Includes bibliographical references and index.

"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"--

ISBN: 9780128150436USD150.00

LCCN: 2019947104Subjects--Topical Terms:

520421
Spatial analysis (Statistics)

LC Class. No.: QA278.2 / .G75 2019

Dewey Class. No.: 519.536

Spatial regression analysis using Eigenvector spatial filtering /
LDR:01589nam a2200193 a 4500 001 2195747
008 200708s2019 enka b 001 0 eng
010 $a 2019947104
020 $a 9780128150436 $q (pbk.) : $c USD150.00
020 $a 0128150432 $q (pbk.)
040 $a DLC $b eng $e aacr2 $c DLC
042 $a pcc
050 # 4 $a QA278.2 $b .G75 2019
082 0 4 $a 519.536 $2 23
100 1 $a Griffith, Daniel. $3 3426119
245 1 0 $a Spatial regression analysis using Eigenvector spatial filtering / $c Daniel A. Griffith, Yongwan Chun, Bin Li ; foreword by Pierre Legendre ; series editor Giuseppe Arbia.
260 # $a London, UK ; $a San Diego, CA, US : $b Academic Press, $c c2019.
300 $a xxviii, 258 p. : $b ill. (some col.) ; $c 23 cm.
490 1 $a Spatial econometrics and spatial statistics
504 $a Includes bibliographical references and index.
520 # $a "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"-- $c Provided by publisher.
650 # 0 $a Spatial analysis (Statistics) $3 520421
650 # 0 $a Regression analysis. $3 529831
650 # 0 $a Eigenvectors. $3 836550
700 1 # $a Chun, Yongwan. $3 2049439
700 1 # $a Li, Bin. $3 834070
700 1 # $a Legendre, Pierre. $3 1532422
700 1 # $a Arbia, Giuseppe. $3 1001058
830 0 $a Spatial econometrics and spatial statistics. $3 3426120