纵向非单调缺失数据下部分线性模型的广义经验似然推断  

Generalized Empirical Likelihood in Partially Linear Modes for Longitudinal With Non-monotone Missing Data

作　　者：刘娟芳[1,2] 薛留根[1] 胡玉琴[1,3] LIU Juanfang;XUE Liugen;HU Yuqin(College of Applied Sciences,Beijing University of Technology,Beijing 100124,China;College of Mathematics and Sciences,Henan Normal University,Xinxiang,Henan 453007,China;School of Data Sciences,Zhejiang University of Finance and Economics,Hangzhou 310018,China)

机构地区：[1]北京工业大学应用数理学院,北京100124 [2]河南师范大学数学与信息科学学院,河南新乡453007 [3]浙江财经大学数据科学学院,杭州310018

出　　处：《北京工业大学学报》2016年第10期1588-1596,共9页Journal of Beijing University of Technology

基　　金：国家自然科学基金资助项目(11331011);北京市自然科学基金资助项目(1142003)

摘　　要：为了研究纵向非单调缺失数据下部分线性模型的估计问题,基于二次推断函数提出了回归系数和基准函数的广义经验似然比函数,得到了相应的极大经验似然估计.证明了所提出的经验对数似然比渐近于卡方分布,由此构造了相应的置信域和逐点置信区间,模拟研究比较了广义经验似然与正态逼近方法的有限样本性质.To study the estimation in partially linear models for longitudinal with non-monotone missing data, based on quadratic inference functions, the generalized empirical likelihood method is used to estimate the regression coefficients and the baseline function, and the corresponding maximum empirical likelihood estimators are derived. The empirical log-likelihood ratios are proven to be asymptotically chisquared, and the corresponding confidence regions and intervals are then constructed. The numerical study is conducted to compare the finite sample behavior of the generalized empirical likelihood and the normal approximation-based method.

关 键 词：纵向数据 非单调缺失 广义经验似然 二次推断函数 

分 类 号：O212.7[理学—概率论与数理统计]