因变量缺失下线性变量含误差模型的估计

主要考虑线性模型在自变量测量含误差以及因变量缺失情况下的估计问题.对于模型中的回归系数,我们基于最小二乘方法提出了两类估计,其中一类估计只由完整观测数据构成,而另外一类估计利用的则是利用简单插补方法构造的完整数据.证明了这两类估计是渐近正态性的.

Estimation in Linear Errors-in-Variables Models with Missing Responses

This paper considers the estimation of linear regression model. We focus on the case where the covariates are measured with additive errors and the response variable is sometimes missing. We propose two estimators of the regression coefficients based on the least-squares approach. The first estimator is constructed using only complete-case observations, the other use simple imputation technique to complete the sample. The resulting estimators are shown to be asymptotically normal and asymptotically equivalent.