政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/153361
English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 113318/144297 (79%)
Visitors : 50955747      Online Users : 936
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
    •  Adv. Search
    Home ‧ Login ‧ Upload ‧ Help ‧ About ‧ Administer Goto mobile version


    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/153361


    Title: 半參數地理加權貝它迴歸模型之建立與應用
    Semi-Parametric Geographically Weighted Beta Regression : Development and Application
    Authors: 韓昕頻
    Han, Sin-Pin
    Contributors: 陳怡如
    Chen, Yi-Ju
    韓昕頻
    Han, Sin-Pin
    Keywords: 地理加權迴歸
    Beta迴歸
    半參數迴歸
    拔靴法
    空間分析
    Geographically weighted regression
    Beta regression
    Semiparametric regression
    Bootstrap
    Spatial analysis
    Date: 2024
    Issue Date: 2024-09-04 14:55:45 (UTC+8)
    Abstract: 根據過往文獻，空間分析的一個重要方向在於探討空間異質性，這種異質性指的是反應變數與解釋變數之間的關係會因地理位置的不同而有所不同。在眾多技術中，地理加權迴歸（Geographically Weighted Regression; GWR）因其計算方法直觀且易於解釋，已成為分析空間異質性時廣泛使用的工具之一。近年來，許多學者對GWR進行了擴展和改進。其中，Silva與Lima於2017年結合Beta迴歸與GWR，提出了地理加權Beta迴歸（Geographically Weighted Beta Regression; GWBR），用於探討反應變數為連續型比例資料之空間異質性。然而，該方法假設所有變數與反應變數之間的關係均會隨空間位置而改變，但此一假設在實際應用中可能並不適用，因為部分變數的迴歸關係可能不會隨空間位置變動。因此，本研究旨在進一步擴展GWBR模型，提出半參數地理加權Beta迴歸模型（semi-parametric GWBR; semi-GWBR），將變數分為全域性與局部性兩類，其中全域性變數對反應變數的影響不隨空間位置改變，而局部性變數則會隨空間位置變動。本研究採用兩階段方法估計semi-GWBR模型的參數，並利用拔靴法計算標準誤，以進行變數的統計推論。此外，本研究還進行了模擬實驗，以評估semi-GWBR模型的估計效果和表現。結果顯示，semi-GWBR模型能夠提供穩定的估計。最後，本研究以2020年6月至2022年6月臺北市各村里大坪數房屋的成交比例作為實證分析資料，將數據分為疫情前後兩個時期，運用semi-GWBR探討疫情前後對臺北市大坪數房屋成交比例的影響因素。研究結果顯示，在此資料的空間異質性分析中，同時存在全域性與局部性變數，且部分變數對大坪數房屋成交比例的影響在疫情前後有所變化；而相比於傳統的Beta迴歸和GWR模型，semi-GWBR模型的表現更為優異。
    Based on previous studies, a key focus in spatial analysis is the exploration of spatial heterogeneity, which refers to how the relationships between a response variable and explanatory variables vary across locations. Among various methods, Geographically Weighted Regression (GWR) has become one of the most widely used tools for analyzing such relationship heterogeneity due to its intuitive computation and ease of interpretation. In recent years, many researchers have extended and improved GWR. In particular, Silva and Lima proposed Geographically Weighted Beta Regression (GWBR), which combines Beta regression with GWR to examine spatial heterogeneity in regression relationships for proportion outcomes in unit interval. However, this method assumes that the relationships between all explanatory variables and the response variable are spatially varying, which may not always be appropriate in real-world applications because some variables may have stable relationships that do not change with location. Therefore, this study aims to extend the GWBR model by introducing a semi-parametric GWBR (semi-GWBR) model. In this model, explanatory variables are classified into global and local categories: the effect of global explanatory variables on the response variable remains constant across space, while the effect of local explanatory variables varies spatially. We use bootstrap methods to estimate standard errors for statistical inference of semi-GWBR parameters. Finally, we apply the model to empirical housing market data from Taipei City to analyze the proportion of large house transactions in different geographical units from June 2020 to June 2022. We divide the data into pre-COVID 19 and post-COVID 19 periods to investigate the factors influencing the proportion of large house transactions in Taipei before and after the pandemic. The results reveal the presence of both global and local factors, with some variables showing different effects after the pandemic. Moreover, the semi-GWBR model outperforms the traditional Beta regression and the GWR model in the current context.
    Reference: [1] David Emanuel Andersson, Oliver F Shyr, and Johnson Fu. Does high-speed rail accessibility influence residential property prices? hedonic estimates from southern taiwan. Journal of Transport Geography, 18(1):166–174, 2010.
    [2] Peter M Atkinson, Sally E German, David A Sear, and Michael J Clark. Exploring the relations between riverbank erosion and geomorphological controls using geographically weighted logistic regression. Geographical Analysis, 35(1):58–82, 2003.
    [3] C. Brunsdon, S. Fotheringham, and M. Charlton. Geographically weighted regression. Journal of the Royal Statistical Society: Series D (The Statistician), 47(3):431 443, 1998.
    [4] Vivian Yi-Ju Chen, Tse-Chuan Yang, and Hong-Lian Jian. Geographically weighted regression modeling for multiple outcomes. Annals of the American Association of Geographers, 112(5):1278–1295, 2022.
    [5] Ying-Hui Chiang, Ti-Ching Peng, and Chin-Oh Chang. The nonlinear effect of convenience stores on residential property prices: A case study of taipei, taiwan. Habitat International, 46:82–90, 2015.
    [6] Alan Ricardo da Silva and Andreza de Oliveira Lima. Geographically weighted beta regression. Spatial Statistics, 21:279–303, 2017.
    [7] Alan Ricardo Da Silva and Thais Carvalho Valadares Rodrigues. Geographically weighted negative binomial regression—incorporating overdispersion. Statistics and Computing, 24:769–783, 2014.
    [8] Silvia Ferrari and Francisco Cribari-Neto. Beta regression for modelling rates and proportions. Journal of applied statistics, 31(7):799–815, 2004.
    [9] A.S. Fotheringham, C. Brunsdon, and M. Charlton. Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley, 2002.
    [10] Paul Harris. A simulation study on specifying a regression model for spatial data: Choosing between autocorrelation and heterogeneity effects. Geographical Analysis, 51(2):151-181, 2019.
    [11] Paul Harris, Chris Brunsdon, Binbin Lu, Tomoki Nakaya, and Martin Charlton. Introducing bootstrap methods to investigate coefficient non-stationarity in spatial regression models. Spatial Statistics, 21:241–261, 2017.
    [12] Dengkui Li and Changlin Mei. A two stage estimation method with bootstrap inference for semi-parametric geographically weighted generalized linear models. International Journal of Geographical Information Science, 32(9):1860–1883, 2018.
    [13] C. Loader. Local Regression and Likelihood. Statistics and Computing. Springer New York, 1999.
    [14] Jeremy Mennis. Mapping the results of geographically weighted regression. The Cartographic Journal, 43(2):171–179, 2006.
    [15] Patrick AP Moran. Notes on continuous stochastic phenomena. Biometrika, 37(1/2):17-23, 1950.
    [16] Tomoki Nakaya. Localspatial interaction modelling based on the geographically weighted regression approach. GeoJournal, 53(4):347–358, 2001.
    [17] Tomoki Nakaya, Alexander S Fotheringham, Chris Brunsdon, and Martin Charlton. Geographically weighted poisson regression for disease association mapping. Statistics in medicine, 24(17):2695–2717, 2005.
    [18] Michael Smithson and Jay Verkuilen. A better lemon squeezer? maximum-likelihood regression with beta-distributed dependent variables. Psychological methods, 11(1):54, 2006.
    [19] Mateusz Tomal and Marco Helbich. A spatial autoregressive geographically weighted quantile regression to explore housing rent determinants in amsterdam and warsaw. Environment and Planning B: Urban Analytics and City Science, 50(3):579–599, 2023.
    [20] David Wheeler and Michael Tiefelsdorf. Multicollinearity and correlation among local regression coefficients in geographically weighted regression. Journal of Geographical Systems, 7(2):161–187, 2005.
    [21] 孫珮齊. 住宅價格分量對其特徵係數變動之研究─以臺中市透天市場為例. 逢甲大學土地管理所碩士論文,2015.
    [22] 張開元. 半嫌惡設施對房價的非線性影響—以台北市消防單位為例. 政治大學財政學系碩士論文,2021.
    [23] 彭建文、張金鶚. 總體經濟對房地產景氣影響之研究. 國家科學委員會研究彙刊:人文及社會科學,10(3):330–343, 2000.
    [24] 彭蒂菁. 超商空間分散度與大台北地區房價之空間分析. 應用經濟論叢,(110):51-104, 2021.
    [25] 彭蒂菁. 醫療可及性是否左右房價? 機器學習之迴歸樹及隨機森林模型的應用. 應用經濟論叢,(109):115–167, 2021.
    [26] 戴國正. 大眾捷運系統對房價影響效果之再檢視.政治大學地政學系碩士論文,2011.
    [27] 李尚華、張金鶚. 建商新推個案產品定位分析-從主力坪數及面積離散度觀點探討.住宅學報,29(2):69–98, 2020.
    [28] 李春長、童作君. 住宅特徵價格模型之多層次分析. 經濟論文叢刊,38(2):289–325, 2010.
    [29] 杜宇璇、宋豐荃、曾禹瑄、葛仲寧、陳奉瑤. 台灣特徵價格模型之回顧分析. 土地問題研究季刊,12(2):44–57, 2013.
    [30] 林崇詠. 影響房地產價格因素之研究:以臺中市西屯區為例.朝陽科技大學財務金融系碩士論文,2015.
    [31] 林忠樑、林佳慧. 學校特徵與空間距離對周邊房價之影響分析-以台北市為例. 經濟論文叢刊,42(2):215–271, 2014.
    [32] 楊宗憲、蘇倖慧. 迎毗設施與鄰避設施對住宅價格影響之研究. 住宅學報, 20(2):61–80, 2011.
    [33] 洪得洋、林祖嘉. 臺北市捷運系統與道路寬度對房屋價格影響之研究. 住宅學報, (8):47–67, 1999.
    [34] 藍鈺婷. 新冠肺炎(covid19)對醫院附近房價的影響－以新竹地區為例. 國立中央大學產業經濟研究所在職專班碩士論文,2021.
    [35] 陳冠甫. 新冠疫情對房價決定因素之影響-以南高雄為例. 國立成功大學財務金融研究所碩士在職專班碩士論文,2023.
    [36] 陳章瑞. 以地理加權迴歸模型之空間分析探討都是公園之寧適效益. 造園景觀學報, 19(1):17–46, 2013.
    [37] 李佳珍、盧永祥、丁安正. 臺北市各區人口,家庭所得與房屋價格關聯性之研究.商業現代化學刊,6(3):243–254, 2012.
    Description: 碩士
    國立政治大學
    統計學系
    111354007
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0111354007
    Data Type: thesis
    Appears in Collections:[Department of Statistics] Theses

    Files in This Item:

    File Description SizeFormat
    400701.pdf3849KbAdobe PDF0View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback