Simultaneous coefficient penalization and model selection in geographically weighted regression: the geographically weighted lasso.
A penalized form of geographically weighted regression (GWR), called the “geographically weighted lasso” (GWL), is presented. The GWL limits the magnitude of the estimated regression coefficients to constrain the impact of explanatory-variable correlation. It also conducts local model selection by...
| Publicado en: | Environment & Planning A Vol. 41; no. 3; pp. 722 - 743 |
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| Formato: | Artículo |
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Pion Limited
March 2009
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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=511401440&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 511401440 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: March 2009 vid: 41 iid: 3 pid: 1065 pub: Pion Limited artinfo: ui: 511401440 10.1068/a40256 ppf: 722 ppct: 21 formats: tig: atl: Simultaneous coefficient penalization and model selection in geographically weighted regression: the geographically weighted lasso. aug: au: Wheeler, David C. su: Geography -- Methodology Geography -- Statistical methods Regression analysis sug: subj: Geography -- Methodology Geography -- Statistical methods Regression analysis ab: A penalized form of geographically weighted regression (GWR), called the “geographically weighted lasso” (GWL), is presented. The GWL limits the magnitude of the estimated regression coefficients to constrain the impact of explanatory-variable correlation. It also conducts local model selection by potentially reducing some of the estimated regression coefficients to zero in some locations of the study area. Two types of GWL are introduced: one designed to enhance prediction of the response variable, and one more geared toward limiting regression coefficients for inference. The application of the GWL to simulated and real datasets demonstrates how it stabilizes regression coefficients in the presence of collinearity. The GWL also yields lower prediction and estimation error of the response variable than does GWR and another constrained version of GWR called geographically weighted ridge regression. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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