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一类半参数可变系数广义线性模型及其拟合 被引量：5

Broad Linear Model of a Set of Semi-parameters and Its Fitting
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摘要 Along the line of the classical generalized linear model,the classical generalized linear model is improved in this article by assuming the partial coefficients of the regressors to be arbitrary functions of the points in some metric space.This new type of regression model is called in this article semiparametric vary coefficient generalized linear model and the back fitting approach is suggested to fit the proposed model,and the smoothing parameter therein are studied.The proposed model not only have higher flexibility and adaptability,but also is suitable for analysis spatial data and therefore has extensive application backgrounds. Along the line of the classical generalized linear model,the classical generalized linear model is improved in this article by assuming the partial coefficients of the regressors to be arbitrary functions of the points in some metric space.This new type of regression model is called in this article semiparametric vary coefficient generalized linear model and the back fitting approach is suggested to fit the proposed model,and the smoothing parameter therein are studied.The proposed model not only have higher flexibility and adaptability,but also is suitable for analysis spatial data and therefore has extensive application backgrounds.
作者 花俊洲 吴冲锋 梅长林
机构地区 上海交通大学管理学院 西安交通大学理学院
出处 《统计研究》 CSSCI 北大核心 2003年第12期57-60,共4页 Statistical Research
关键词 广义线性模型 半参数可变系数 义线性模型 后向拟合法
分类号 O212 [理学—概率论与数理统计]
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参考文献8

  • 1Nelder. J. A. and Wedderburn.. R. W. M(1972) Generalized linear models J. R. Statist. Soc. A. 135. 370 - 384.
  • 2Brunsdon. C., Fotheringham. A. S and Charlton. M. (1999)Some notes on parametric significance test for geographically weighted regression. Journal of Regional Science. Vol. 39:497 ～ 524.
  • 3Tibshirani. R. and Hastie. T. , ( 1987 ) Local likelihood estimation. J. Am. Statist. Ass., 82. 559 - 567.
  • 4Cox. D. R. (1972) Regression models and life-tables ( with discussion). J. R. Statist. Soc. B., 34,187 - 220.
  • 5Fan. J. and Gijbels, I. ( 1995 ) Data-driven bandwidth selection in local ploynomial fitting: variale bandwidth and spatial adaptation. J. R. Statist. Soc. B., 57: 371 ～ 394.
  • 6花俊洲,梅长林,吴冲锋.变系数广义线性模型及其估计[J].系统科学与数学,2004,24(1):41-50. 被引量：14
  • 7Hastie. T. Tibshirani. R. ( 1990 ) Generalized additive models. London: Chapman and Hall.
  • 8Jorgensen, B. ( 1983 ). Maximum likelihood estimation and large-sample inference for generalized linear and! nonlinear models. Biometrika 70 19 ～ 28.

二级参考文献12

  • 1Nelder J A and Wedderburn R W M. Generalized linear models. J. R. Statist. Soc. A., 1972, 135:370-384.
  • 2Brunsdon C, Fotheringham A S, Charlton M. Geographically weighted regression: a method for exploring spatial nonstationarity. Geographical Analysis., 1996, 28: 281-298.
  • 3Brunsdon C, Fotheringham A S, Charlton M. Geographically weighted regression --modelling spatial non-stationarity. The Statistician, 1998, 47: 431-433.
  • 4Green P J and Silverman B W. Nonparametric Regression and Generalized Linear Models. London,Chapman and Hall, 1994.
  • 5Loader C. Local Regression and Likelihood. Springer-Verlag, New York, 1999.
  • 6Silverman B W. Densityratios empirical likelihood and cot death. Apple. Statist., 1978, 27: 26-33.
  • 7West M, Harrison P J. Bayesian Forcasting and Dynamic Models. Slaringer, New York, 1989.
  • 8Tibshirani R and Hastie T. Local likelihood estimation. J. Am. Statist. Ass.,1987,82:559-567
  • 9Cox D R. Regression models and life-tables(with discussion). J. R. Statist. Soc. B., 1972, 34:187-220.
  • 10Fan J and Gijbels I. Data-driven bandwidth selection in local ploynomial fitting : variale bandwidth and spatial adaptation. J. R. Statist. Soc. B., 1995, 57: 371-394.

共引文献13

同被引文献65

引证文献5

二级引证文献118

统计研究
2003年 第12期

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