部分线性EV模型的惩罚经验似然  

Penalized empirical likelihood for partially linear errors-in-variables models

作　　者：毛沥悦 陈夏[1] MAO Liyue;CHEN Xia(School of Mathematics and Information Science,Shaanxi Normal University,Xi′an 710119,Shaanxi,China)

机构地区：[1]陕西师范大学数学与信息科学学院,陕西西安710119

出　　处：《陕西师范大学学报（自然科学版）》2020年第2期92-98,共7页Journal of Shaanxi Normal University：Natural Science Edition

基　　金：教育部人文社会科学研究青年基金(18YJC910003)。

摘　　要：研究了当非参数部分带有测量误差时,部分线性模型的变量选择与参数估计问题。在自适应Lasso惩罚函数下,证明了所构造的惩罚经验似然估计具有Oracle性质。同时,考虑了参数的假设检验问题。最后利用数值模拟说明了所提方法的优良性质。It is considered that penalized empirical likelihood for parameter estimation and variable selection in partially linear models with measurement errors in non-parametric part. By using adaptive Lasso penalty function, it is shown that the proposed estimator has the oracle property.Also, the problem of testing hypothesis is considered. Some simulations are given to illustrate the performance of the proposed method.

关 键 词：惩罚经验似然 测量误差 变量选择 部分线性模型 

分 类 号：O212.1[理学—概率论与数理统计]