淡江大學機構典藏:Item 987654321/120280
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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/120280


    Title: Geographically Weighted Regression Analysis for Multivariate Response
    Other Titles: 多個反應變數之地理加權迴歸分析
    Authors: Chen, Vivian Yi-Ju
    Keywords: 多反應變數;多變量迴歸;地理加權迴歸;空間異值性;空間分析
    Date: 2020-12-10
    Issue Date: 2021-03-19 12:11:26 (UTC+8)
    Abstract: Geographically weighted regression (GWR) has been a popular tool widely applied in various disciplines to explore spatial nonstationarity for georeferenced data. Such technique, however, typically restricts the analysis on a single outcome variable to reveal its spatial nonstationary pattern explaining with a set of explanatory variables. When it comes to model multiple interrelated response variables, GWR fails to provide sufficient information of the data as it only allows the separate modeling for each response variable. This study attempts to address the gap by introducing a geographically weighted multivariate multiple regression (GWMMR) technique capable to explore spatial nonstationarity but also to account correlations across multivariate responses. We present the model specification of the proposed method and construct the associated statistical inferences. Certain related modeling issues which include the test of spatial nonstationarity and a semiparametric version of the GWMMR are also discussed. For an empirical illustration, the new technique is applied to the stop-and-frisk data published by the New York Police Department. The analysis results and prediction performance are then compared with those obtained by other existing analytical tools. The results demonstrate the usefulness of the GWMMR in that it can examine the differences possibly overlooked in univariate analysis and understand the multiple outcomes as a system rather than isolated investigations.
    Relation: 2020 台灣地理資訊學會年會暨學術研討會
    Appears in Collections:[Graduate Institute & Department of Statistics] Proceeding

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