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高校应用数学学报
高校应用数学学报  2017, Vol. 32 Issue (3): 267-276    
    
混合双重广义线性模型的参数估计
袁巧莉, 吴刘仓, 戴琳
昆明理工大学 理学院, 云南昆明 650093
Parameters estimation for mixture of double generalized linear models
YUAN Qiao-li, WU Liu-cang, DAI Lin
Faculty of Science, Kunming University of Science and Technology, Kunming 650093, China
 全文: PDF 
摘要： 在实际应用中, 不同类别的数据统计特性存在差异, 所以对异质总体的研究非常有必要. 基于总体一, 二阶矩存在, 利用双重广义线性模型对异质总体的不同子类数据的均值和散度同时建模, 研究提出了混合双重广义线性模型. 然后, 利用EM算法构造了模型参数的最大扩展拟似然估计和最大伪似然估计. 最后, 通过随机模拟和实例研究, 结果表明模型和方法的有效性和有用性.
关键词： 混合双重广义线性模型;  最大扩展拟似然估计;  最大伪似然估计;  EM算法    
Abstract: In applications, there are many different statistical characteristics among diverse categories, so it is very necessary to study the heterogeneous population. This paper is based on the existence of the first order and second order moments of the distribution function, and the mixture of double generalized linear models is used to build mean and variance models in different population. After constructing the extended quasi-likelihood and pseudo-likelihood functions, the EM algorithm is used to estimate the mean parameter, dispersion parameter and mixture proportion. Finally, a Monte Carlo experiment and a real example prove that the model and the method are effective.
Key words: mixture of double generalized linear models    maximum extended quasi-likelihood estimation    maximum Pseudo-likelihood estimation    EM algorithm
收稿日期: 2016-10-06 出版日期: 2018-04-07
:  O212.1  
基金资助: 国家自然科学基金(11261025; 11026309)
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袁巧莉
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引用本文:

袁巧莉, 吴刘仓, 戴琳. 混合双重广义线性模型的参数估计[J]. 高校应用数学学报, 2017, 32(3): 267-276.

YUAN Qiao-li, WU Liu-cang, DAI Lin. Parameters estimation for mixture of double generalized linear models . Applied Mathematics A Journal of Chinese Universities, 2017, 32(3): 267-276.

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http://www.zjujournals.com/amjcua/CN/    或    http://www.zjujournals.com/amjcua/CN/Y2017/V32/I3/267

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