教師資料查詢 | 類別: 期刊論文 | 教師: 鄧文舜 Deng Wen-shuenn (瀏覽個人網頁)

標題:Geographically Weighted Quantile Regression (GWQR): An Application to U.S. Mortality Data
學年
學期
出版(發表)日期2012/04/01
作品名稱Geographically Weighted Quantile Regression (GWQR): An Application to U.S. Mortality Data
作品名稱(其他語言)
著者Chen, Vivian Yi-Ju; Deng, Wen-Shuenn; Yang, Tse-Chuan; Matthews, Stephen A.
單位淡江大學統計學系
出版者Hoboken: Wiley-Blackwell Publishing, Inc.
著錄名稱、卷期、頁數Geographical Analysis 44(2), pp.134–150
摘要In recent years, techniques have been developed to explore spatial nonstationarity and to model the entire distribution of a regressand. The former is mainly addressed by geographically weighted regression (GWR), and the latter by quantile regression (QR). However, little attention has been paid to combining these analytical techniques. The goal of this article is to fill this gap by introducing geographically weighted quantile regression (GWQR). This study briefly reviews GWR and QR, respectively, and then outlines their synergy and a new approach, GWQR. The estimations of GWQR parameters and their standard errors, the cross-validation bandwidth selection criterion, and the nonstationarity test are discussed. We apply GWQR to U.S. county data as an example, with mortality as the dependent variable and five social determinants as explanatory covariates. Maps summarize analytic results at the 5, 25, 50, 75, and 95 percentiles. We found that the associations between mortality and determinants vary not only spatially, but also simultaneously across the distribution of mortality. These new findings provide insights into the mortality literature, and are relevant to public policy and health promotion. Our GWQR approach bridges two important statistical approaches, and facilitates spatial quantile-based statistical analyses.
關鍵字
語言英文
ISSN1538-4632
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國別美國
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