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A two-stage estimation method with bootstrap inference for semi-parametric geographically weighted generalized linear models / Dengkui Li in International journal of geographical information science IJGIS, vol 32 n° 9-10 (September - October 2018)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 32 n° 9-10 (September - October 2018) . - pp 1860 - 1883 Titre : A two-stage estimation method with bootstrap inference for semi-parametric geographically weighted generalized linear models Type de document : Article/Communication Auteurs : Dengkui Li, Auteur ; Chang-Lin Mei, Auteur Année de publication : 2018 Article en page(s) : pp 1860 - 1883 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] estimation statistique [Termes IGN] inférence statistique [Termes IGN] méthode du maximum de vraisemblance (estimation) [Termes IGN] modèle linéaire [Termes IGN] population urbaine [Termes IGN] régression géographiquement pondérée [Termes IGN] simulation [Termes IGN] Tokyo (Japon) Résumé : (Auteur) Semi-parametric geographically weighted generalized linear models (S-GWGLMs) are a useful tool in modeling a regression relationship where the impact of certain explanatory variables on a non-Gaussian distributed response variable is global while that of others is spatially varying. In this article, we propose for S-GWGLMs a new estimation method, called two-stage geographically weighted maximum likelihood estimation, and further develop a likelihood ratio statistic-based bootstrap test to determine constant coefficients in the models. The performance of the estimation and test methods is then evaluated by simulations. The results show that the proposed estimation method performs as well as the existing method in estimating both constant and spatially varying coefficients but it is more efficient in terms of computation time; the bootstrap test is of accurate size under the null hypothesis and satisfactory power in identifying spatially varying coefficients. A real-world data set is finally analyzed to demonstrate the application of the proposed estimation and test methods. Numéro de notice : A2018-306 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1463443 Date de publication en ligne : 03/05/2018 En ligne : https://doi.org/10.1080/13658816.2018.1463443 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90449 [article]

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A bootstrap test for constant coefficients in geographically weighted regression models / Chang-Lin Mei in International journal of geographical information science IJGIS, vol 30 n° 7- 8 (July - August 2016)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 30 n° 7- 8 (July - August 2016) . - pp 1622 - 1643 Titre : A bootstrap test for constant coefficients in geographically weighted regression models Type de document : Article/Communication Auteurs : Chang-Lin Mei, Auteur ; Min Xu, Auteur ; Ning Wang, Auteur Année de publication : 2016 Article en page(s) : pp 1622 - 1643 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques [Termes IGN] base de données déductive [Termes IGN] Bootstrap (EDI) [Termes IGN] inférence statistique [Termes IGN] modèle de régression [Termes IGN] processeur [Termes IGN] régression géographiquement pondérée [Termes IGN] test de performance Résumé : (Auteur) Statistical tests for whether some coefficients really vary over space play an important role in using the geographically weighted regression (GWR) to explore spatial non-stationarity of the regression relationship. In view of some shortcomings of the existing inferential methods, we propose a residual-based bootstrap test to detect the constant coefficients in a GWR model. The proposed test is free of the assumption that the model error term is normally distributed and admits some useful extensions for identifying more complicated spatial patterns of the coefficients. Some simulation with comparison to the existing test methods is conducted to assess the test performance, including the accuracy of the bootstrap approximation to the null distribution of the test statistic, the power in identifying spatially varying coefficients and the robustness to collinearity among the explanatory variables. The simulation results demonstrate that the bootstrap test works quite well. Furthermore, a real-world data set is analyzed to illustrate the application of the proposed test. Numéro de notice : A2016-320 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2016.1149181 En ligne : http://dx.doi.org/10.1080/13658816.2016.1149181 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80940 [article]

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079-2016042	RAB	Revue	Centre de documentation	En réserve L003	Disponible
079-2016041	RAB	Revue	Centre de documentation	En réserve L003	Disponible