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政大機構典藏 > 商學院 > 統計學系 > 期刊論文 > Item 140.119/160211

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/160211

Title:	Geographically weighted regression modeling for multiple outcomes
Authors:	陳怡如 Chen, Vivian Yi-Ju;Yang, Tse-Chuan;Jian, Hong-Lian
Contributors:	統計系
Keywords:	geographically weighted regression;multiple outcomes;multivariate multiple regression;spatial nonstationarity
Date:	2022-01
Issue Date:	2025-11-14
Abstract:	Geographically weighted regression (GWR) has been a popular tool applied in various disciplines to explore spatial nonstationarity for georeferenced data. Such a technique, however, typically restricts the analysis to a single outcome variable and a set of explanatory variables. When analyzing multiple interrelated response variables, GWR fails to provide sufficient information about the data because it only allows separate modeling for each response variable. This study attempts to address this gap by introducing a geographically weighted multivariate multiple regression (GWMMR) technique that not only explores spatial nonstationarity but also accounts for correlations across multivariate responses. We first present the model specification of the proposed method and then draw the associated statistical inferences. Several modeling issues are discussed. We also examine finite sample properties of GWMMR using simulation. For an empirical illustration, the new technique is applied to the stop-and-frisk data published by the New York Police Department. The results demonstrate the usefulness of the GWMMR.
Relation:	Annals of the American Association of Geographers, Vol.112, No.5, pp.1278-1295
Data Type:	article
DOI 連結:	https://doi.org/10.1080/24694452.2021.1985955
DOI:	10.1080/24694452.2021.1985955
Appears in Collections:	[統計學系] 期刊論文

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