Minimax least squares and quasiminimax estimation in linear models with missing values

被引：0

作者：

Janner, M

Stahlecker, P

机构：

[1] UNIV OLDENBURG, DEPT ECON, OLDENBURG, GERMANY

[2] UNIV HAMBURG, DEPT ECON, W-2000 HAMBURG, GERMANY

来源：

ACTA APPLICANDAE MATHEMATICAE | 1996年 / 43卷 / 01期

关键词：

linear regression model; missing values; prior information; minimax estimation; error in variables;

D O I：

10.1007/BF00046997

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

We consider the problem of estimating the parameter vector in the linear model when observations on the independent variables are partially missing or incorrect. New estimators are developed, which systematically combine prior information with the incomplete data. We compare these methods with the alternative strategy of deleting incomplete observations.

引用

页码：159 / 167

页数：9