Local linear estimation of spatially varying coefficient models: an improvement on the geographically weighted regression technique.
The writers propose a local linear-based geographically weight regression (GWR) for the spatially varying coefficient models. They explain that in the proposed method the coefficients are locally expanded as linear functions of the spatial coordinates and then estimated by the weighted least-square...
| Publicado en: | Environment & Planning A Vol. 40; no. 4; pp. 986 - 1006 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Pion Limited
April 2008
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=511374919&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 511374919 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 0308518X EPL jtl: Environment & Planning A issn: 0308518X maglogo: N pubinfo: dt: April 2008 vid: 40 iid: 4 pid: 1065 pub: Pion Limited artinfo: ui: 511374919 10.1068/a3941 ppf: 986 ppct: 20 formats: tig: atl: Local linear estimation of spatially varying coefficient models: an improvement on the geographically weighted regression technique. aug: au: Wang, Ning Mei, Chang-Lin Yan, Xiao-Dong su: Geography -- Methodology Geography -- Statistical methods Regression analysis sug: subj: Geography -- Methodology Geography -- Statistical methods Regression analysis ab: The writers propose a local linear-based geographically weight regression (GWR) for the spatially varying coefficient models. They explain that in the proposed method the coefficients are locally expanded as linear functions of the spatial coordinates and then estimated by the weighted least-squares procedure. Based on some theoretical and numerical comparisons with GWR, they conclude that the proposed method can significantly enhance GWR not only in terms of goodness-of-fit of the entire regression function but also in lowering bias of the coefficient estimates. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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