A general framework for estimation and inference of geographically weighted regression models: 2. Spatial association and model specific tests.

A development that casts geographically weighted regression (GWR) as a model of locational heterogeneity is presented in order to formulate a general model of spatial effects that includes as special cases GWR with a spatially lagged objective variable and GWR with spatial error autocorrelation. It...

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Publicado en:Environment & Planning A Vol. 34; no. 5; pp. 883 - 905
Autores principales: Páez, Antonio, Uchida, Takashi, Miyamoto, Kazuaki
Formato: Artículo
Publicado: Pion Limited May 2002
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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        atl: A general framework for estimation and inference of geographically weighted regression models: 2. Spatial association and model specific tests.
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        au:
          Páez, Antonio
          Uchida, Takashi
          Miyamoto, Kazuaki
      su:
        Spatial analysis (Statistics)
        Geography -- Methodology
        Geography -- Statistical methods
        Regression analysis
      sug:
        subj:
          Spatial analysis (Statistics)
          Geography -- Methodology
          Geography -- Statistical methods
          Regression analysis
      ab: A development that casts geographically weighted regression (GWR) as a model of locational heterogeneity is presented in order to formulate a general model of spatial effects that includes as special cases GWR with a spatially lagged objective variable and GWR with spatial error autocorrelation. It is indicated that such an approach allows derivation of formal tests against several forms of model misspecification.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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