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缺失数据下含几何分布的对数线性模型的EM算法(英文) 被引量：1

The EM Algorithm in Logistic Linear Models with Geometric Distribution Involving Missing Data
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摘要 本文研究缺失数据下对数线性模型参数的极大似然估计问题.通过Monte-Carlo EM算法去拟合所提出的模型.其中,在期望步中利用Metropolis-Hastings算法产生一个缺失数据的样本,在最大化步中利用Newton-Raphson迭代使似然函数最大化.最后,利用观测数据的Fisher信息得到参数极大似然估计的渐近方差和标准误差. In this paper,a geometric response and normal covariace model for the missing data are assumed. We fit the model using the Monte Carlo EM（Expectation and Maximization） algorithm. The E-step is derived by Metropolis-Hastings algorithm to generate a sample for missing data, and the M-Step is done by Newton-Raphson to maximize the likelihood function. Asymptotic variances and the standard errors of the MLE of parameters are derived using the observed Fisher information.
作者 王继霞 刘次华
机构地区 河南师范大学数学与信息科学学院 华中科技大学数学与统计学院
出处 《应用数学》 CSCD 北大核心 2009年第2期297-302,共6页 Mathematica Applicata
基金 Supported by the National Science Foundation of China(10671057)
关键词 条件期望 极大似然估计 EM算法 Metropolis—Hastings算法 Newton—Raphson迭代 Conditional expectation Maximum likelihood estimation EM algorithmMetropolis-Hastings algorithm Newton-Raphson iteration
分类号 O212.7 [理学—概率论与数理统计]
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参考文献10

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同被引文献11

  • 1Aitkin M. Modelling variance heterogeneity in normal regression using GLIM[J].Applied Statistics,1987.332-339.
  • 2Taylor J T,Verbyla A P. Joint modelling of location and scale parameters of the t distribution[J].{H}STATISTICAL MODELLING,2004.91-112.
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  • 4WU Liucang,ZHANG Zhongzhan,XU Dengke. Variable selection in joint location and scale models of the skew-normal distribution[J/OL].Journal of Statistical Computation and Simulations,2012.
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  • 6XIE Fengchang,WEI Bocheng,LIN Jinguan. Homogeneity diagnostics for skew-normal nonlinear regression models[J].{H}Statistics & Probability Letters,2009,(6):821-827.
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  • 10童恒庆,余超,赵旭杰.凸约束广义线性回归模型的参数估计及算法[J].应用数学,2008,21(4):635-639. 被引量：5

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应用数学
2009年 第2期

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