Geographically weighted regression modeling for multiple outcomes

doi:10.1080/24694452.2021.1985955

淡江大學機構典藏 > 商管學院 > 統計學系暨研究所 > 期刊論文 > Item 987654321/122077

jsp.display-item.identifier=請使用永久網址來引用或連結此文件: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/122077

题名:	Geographically weighted regression modeling for multiple outcomes
作者:	Chen, Vivian Yi-Ju;Yang, Tse-Chuan;Jian, Hong-Lian
关键词:	geographically weighted regression;multiple outcomes;multivariate multiple regression;spatial nonstationarity
日期:	2022-01-07
上传时间:	2022-01-25 12:10:18 (UTC+8)
摘要:	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.
關聯:	Annals of the Association of American Geographer
DOI:	10.1080/24694452.2021.1985955
显示于类别:	[統計學系暨研究所] 期刊論文

文件中的档案:

档案	描述	大小	格式	浏览次数
index.html		0Kb	HTML	62	检视/开启

在機構典藏中所有的数据项都受到原著作权保护.

TAIR相关文章

数据加载中.....