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空间自相关地理加权回归模型的估计 被引量:27

Estimation in Gegorapically Weighted Regression with Spatial Autocorrelation
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摘要 地理加权回归作为一类能有效处理回归分析中空间非平稳性现象的建模技术,在多类问题的研究得到了广泛的应用.主要讨论这类空间计量经济学模型在空间自相关情形下的估计问题.首先,对于因变量含有空间滞后项的地理加权回归模型,分别给出了局部似然估计和两步估计两种方法.其次,考虑了误差空间自相关下地理加权回归模型的估计问题. Geographically weighted regression(GWR),as a useful method for exploring spatial non- stationarity of a regression relationship,has been applied to a variety of areas. In this paper,This paper considers the estimation of this spatial economtrics model when spatial autocorrelation is available.Firstly,we propose a geographically weighted autogressive models and provide local likelihood and two-step method estimating procedures.Secondly, the estimation of geographically weighted regression model's with spatial correlated errors is discussed.
出处 《数学的实践与认识》 CSCD 北大核心 2010年第22期126-134,共9页 Mathematics in Practice and Theory
基金 国家社会科学基金项目"空间计量经济学:理论与应用(07CTJ003)"的阶段性研究成果 中央民族大学"211"项目(021211030312)
关键词 地理加权回归模型 空间自相关 局部似然 两步估计 〈Keyword〉gegorapically weighted regression spatial autocorrelation local likelihood two-step method
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参考文献14

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二级参考文献26

  • 1Mei Changlin Wang NingSchool of Science,Xi’an Jiaotong Univ.,Xi’an 710049..FUNCTIONAL-COEFFICIENT REGRESSION MODEL AND ITS ESTIMATION[J].Applied Mathematics(A Journal of Chinese Universities),2001,16(3):304-314. 被引量:6
  • 2魏传华,梅长林.半参数空间变系数回归模型的两步估计方法及其数值模拟[J].统计与信息论坛,2005,20(1):16-19. 被引量:27
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  • 6Brunsdon C, Fotheringham A S, Charlton M. Geographically weighted regression: a method for exploring spatial nonstationarity[J]. Geographical Analysis, 1996, 28: 281-298.
  • 7Fotheringham A S, Charlton M, Brunsdon C. Measuring spatial variation in relationships with geographically weighted regression[J]. In Recent Developments in Spatial Analysis, Edited by M M Fischer and A Getis,Springer-Verlag, London, 1997. 60-82.
  • 8Leung Yee. Mei Changlin, Zhang Wenxiu. Statistical tests for spatial nonstationarity based on the geographically weighted regression model[J]. Environment and Planning A, 2000, 32: 9-32.
  • 9Mei Changlin, He Shuyuan, Fang Kaitai. A note on the mixed geographically weighted regression model[J].Journal of Regional Science A, 2004, 44: 143-157.
  • 10Bowman A W. An alternative method of cross-validation for the smoothing of density estimate[J]. Biometrika,1984, 71: 353-360.

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