Mean Squared Error Matrix comparison of Least Squares and Stein-Rule Estimators for Regression Coefficients under Non-normal Disturbances

Shalabh; Toutenburg, Helge und Heumann, Christian (2006): Mean Squared Error Matrix comparison of Least Squares and Stein-Rule Estimators for Regression Coefficients under Non-normal Disturbances. Collaborative Research Center 386, Discussion Paper 496

Abstract

Choosing the performance criterion to be mean squared error matrix, we have compared the least squares and Stein-rule estimators for coefficients in a linear regression model when the disturbances are not necessarily normally distributed. It is shown that none of the two estimators dominates the other, except in the trivial case of merely one regression coefficient where least squares is found to be superior in comparisons to Stein-rule estimators.

• Item Type: Paper
• Keywords: Linear regression model, Stein rule estimator, ordinary least squares estimator, mean squared error matrix.
• Faculties: Mathematics, Computer Science and Statistics > Statistics > Collaborative Research Center 386; Special Research Fields > Special Research Field 386
• Subjects: 500 Science > 510 Mathematics
• Language: English