因变量缺失下变系数部分线性测量误差模型的变量选择

主要研究因变量存在缺失且协变量部分包含测量误差情形下,如何对变系数部分线性模型同时进行参数估计和变量选择.我们利用插补方法来处理缺失数据,并结合修正的profile最小二乘估计和SCAD惩罚对参数进行估计和变量选择.并且证明所得的估计具有渐近正态性和Oracle性质.通过数值模拟进一步研究所得估计的有限样本性质.

Variable Selection for Varying Coefficient Partially Linear Models with Errors-in-covariates and Missing Response

This paper focuses on simultaneous parameter estimation and variable selection to varying-coefficient partially Unear models when covariates of parametric component are measured with additive errors and responses are missing.The imputation method is applied to handle missing responses,and a variable selection procedure is proposed by using corrected profile least-squares method and SCAD penalization,and the resulting estimators perform asymptotic normality as well as the oracle property.Simulation studies are conducted to illustrate the finite sample properties of the proposed procedures.