Diagnostics in generalized nonlinear models based on maximum L_q-likelihood estimation  

基于最大L_q似然估计的广义非线性模型的统计诊断(英文)

作　　者：徐伟娟[1] 林金官[1] 

机构地区：[1]东南大学数学系,南京211189

出　　处：《Journal of Southeast University(English Edition)》2013年第1期106-110,共5页东南大学学报（英文版）

基　　金：The National Natural Science Foundation of China(No.11171065);the Natural Science Foundation of Jiangsu Province(No.BK2011058)

摘　　要：In order to detect whether the data conforms to the given model, it is necessary to diagnose the data in the statistical way. The diagnostic problem in generalized nonlinear models based on the maximum Lq-likelihood estimation is considered. Three diagnostic statistics are used to detect whether the outliers exist in the data set. Simulation results show that when the sample size is small, the values of diagnostic statistics based on the maximum Lq-likelihood estimation are greater than the values based on the maximum likelihood estimation. As the sample size increases, the difference between the values of the diagnostic statistics based on two estimation methods diminishes gradually. It means that the outliers can be distinguished easier through the maximum Lq-likelihood method than those through the maximum likelihood estimation method.为了检测数据是否符合给定的模型,需要对数据进行统计诊断.研究了基于最大Lq似然估计的广义非线性模型的统计诊断问题.利用3个统计诊断量来检验数据中是否都存在异常点.模拟结果显示,当样本容量较小时,使用最大Lq似然估计方法得到的诊断统计量的结果要比使用极大似然估计(MLE)方法得到的结果大;随着样本容量的增加,它们之间的区别逐渐减小.因此,使用最大Lq似然估计方法比用MLE方法更容易找到数据中的异常点.

关 键 词：maximum Lq-likelihood estimation generalized nonlinear regression model case-deletion model generalized Cook distance likelihood distance difference of deviance 

分 类 号：O212.4[理学—概率论与数理统计]