基于广义线性模型的岭型主成分估计及实例分析  

Ridge Type Principal Component Estimation Based on Generalized Linear Models and its Analysis of the Practical Example

作　　者：王文军[1] 杨幼玲 Wang Wenjun Yang Youling(Northeast Forestry Universit)

机构地区：[1]东北林业大学

出　　处：《哈尔滨师范大学自然科学学报》2016年第3期18-21,共4页Natural Science Journal of Harbin Normal University

摘　　要：在广义线性模型已有的参数估计的基础上,结合广义线性模型中的主成分估计和岭估计,给出了广义线性模型中的新的估计方法岭型主成分估计,并在均方误差意义下证明了在满足一定条件下这种估计方法是优于广义线性模型中的最大似然估计、主成分估计、岭估计的,最后通过实际算例对比分析验证了理论上得到的结果.In this paper, on the basis of the existing parameter estimation of the generalized linear model, by combining principal component estimation and ridge estimation in generalized linear model , a new estimation method ridge type the principal component estimation of generalized linear model is presented. And in the sense of Mean Square Error （MSE） it proves that this estimation method is better than the maximum likelihood estimation, principal component estimation and ridge estimation in the generalized linear model under certain conditions . At last the theory of this paper is verified by practical example analysis results.

关 键 词：广义线性模型 岭型主成分估计 

分 类 号：O212[理学—概率论与数理统计]