Estimation of linear regression models with missing data: The role of stochastic linear constraints

被引：5

作者：

Toutenburg, S

Toutenburg, H ^{[1
]}

机构：

[1] Univ Munich, Dept Stat, D-80799 Munich, Germany

[2] Indian Inst Technol, Dept Math, Kanpur, Uttar Pradesh, India

来源：

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS | 2005年 / 34卷 / 02期

关键词：

linear regression model; missing data; stochastic linear constraints;

D O I：

10.1081/STA-200047425

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

Assuming the non availability of some observations and the availability of some stochastic linear constraints connecting the coefficients in a linear regression, the technique of mixed regression estimation is considered and a set of five unbiased estimators for the vector of coefficients is presented. They are compared with respect to the criterion of variance covariance matrix and conditions are obtained for the superiority of one estimator over the other.

引用

页码：375 / 387

页数：13

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