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McMillen, D.P. and McDonald, J.F. (2004) Locally Weighted Maximum-Likelihood Estimation: Monte Carlo Evidence and an Application. In: Anselin, L., Florax, R.J.G.M. and Rey, S.J., Eds., Advances in Spatial Econometrics: Methodology, Tools and Applications, Springer, Heidelberg.
http://dx.doi.org/10.1007/978-3-662-05617-2_10

被以下文章引用:

• 标题: 关于空间回归模型残差项分布未知的研究Research on Spatial Regression Model with Unknown Distribution Residuals

  作者: 滕德雄, 张辉国, 胡锡健

  关键字: 剖面似然方法, 残差项分布未知, 空间回归模型Pro?le Likelihood Method, Unknown Distribution Residuals, Spatial Regression Model

  期刊名称: 《Statistics and Application》, Vol.5 No.2, 2016-06-17

  摘要: 文章分析了剖面似然方法，在残差项分布未知的条件下研究了空间回归模型的回归系数的估计问题，并在模拟实验中与二阶段最小二乘方法做比较，得出剖面似然方法所得到的参数估计更加稳健的结论。 This paper analyzed the profile likelihood method, mainly studied the estimation of regression coefficients under the condition of unknown distribution of residuals of spatial regression model and made some simulation experiments comparing with two-stage least squares method. The simu-lation obtained the conclusions that the parameter estimated by profile likelihood method dem-onstrated more robust.