A general framework for estimation and inference of geographically weighted regression models: 1. Location-specific kernel bandwidths and a test for locational heterogeneity.
The writers argue that placing geographically weighted regression (GWR) within a statistical context as a spatial model of error variance heterogeneity, or what might be called locational heterogeneity, solves the problems associated with developing GWR along the lines of local regression and smooth...
| Publicado en: | Environment & Planning A Vol. 34; no. 4; pp. 733 - 755 |
|---|---|
| Autores principales: | , , |
| Formato: | Artículo |
| Publicado: |
Pion Limited
April 2002
|
| Materias: | |
| 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=513135210&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 513135210 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 2002 vid: 34 iid: 4 pid: 1065 pub: Pion Limited artinfo: ui: 513135210 10.1068/a34110 ppf: 733 ppct: 22 formats: tig: atl: A general framework for estimation and inference of geographically weighted regression models: 1. Location-specific kernel bandwidths and a test for locational heterogeneity. aug: au: Páez, Antonio Uchida, Takashi Miyamoto, Kazuaki su: Regression analysis Geography -- Methodology Geography -- Statistical methods sug: subj: Regression analysis Geography -- Methodology Geography -- Statistical methods ab: The writers argue that placing geographically weighted regression (GWR) within a statistical context as a spatial model of error variance heterogeneity, or what might be called locational heterogeneity, solves the problems associated with developing GWR along the lines of local regression and smoothing techniques. They present a maximum-likelihood-based framework for estimation and inference of a general GWR model that leads to a method for estimating location-specific kernel bandwidths. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
|---|