因变量缺失下线性回归模型的岭估计
Ridge Estimation of Linear Regression Model with Missing Responses
DOI: 10.12677/SA.2022.116158, PDF,    科研立项经费支持
作者: 李 静：中国劳动关系学院应用技术学院，北京；安佰玲：淮北师范大学数学科学学院，安徽 淮北
关键词: 线性回归模型；缺失数据；插补方法；最小二乘估计；岭估计；Linear Regression； Missing Data； Imputation Method； Least-Squares Estimation； Ridge Estimation
摘要: 本文研究线性回归模型存在多重共线性且因变量存在缺失时的估计问题，分别基于完整数据方法和单点插补方法，给出了回归系数的两种岭估计，给出了估计量的渐近性质。最后通过数值模拟验证了所提方法的有效性。
Abstract: This paper discusses estimation of linear regression models in the presence of multicollinearity and the responses are missing at random. Based on the complete-case method and single imputation technique, two ridge estimators for the unknown regression coefficients are proposed. Asymptotically properties of the proposed estimators are given. Finally, some simulations are conducted to illustrate the proposed methods.
文章引用：李静, 安佰玲. 因变量缺失下线性回归模型的岭估计[J]. 统计学与应用, 2022, 11(6): 1515-1520. https://doi.org/10.12677/SA.2022.116158

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