应用数学2016，Vol.29Issue(2)：252-257,6.
缺失数据下双重广义线性模型的经验似然推断
Empirical Likelihood for Double Generalized Linear Models with Missing Data
摘要
Abstract
Based on profiled empirical likelihood method,the double generalized linear models' quasi-likelihood functions were considered as the constraints of the profile empirical likelihood ratio function with missing responses.The confidence intervals of unknown parameters in double generalized linear models were constructed.Simulation studies show that,in the double generalized linear models,the inverse probability weighted method and empirical likelihood method are more useful and effective than the unweighted method and normal approximation method respectively.关键词
缺失数据/双重广义线性模型/经验似然/置信区间Key words
Missing Data/Double generalized linear model/Empirical likelihood/Confidence interval分类
数理科学引用本文复制引用
吴刘仓,王子豪,詹金龙..缺失数据下双重广义线性模型的经验似然推断[J].应用数学,2016,29(2):252-257,6.基金项目
国家自然科学基金项目(11261025) （11261025）
国家自然科学基金项目(11126309) （11126309）
云南省自然科学基金项目(2011FZ044) （2011FZ044）
