A note on the comparison of minimax linear and mixed regression estimation of regression coefficients when prior estimates are available

Toutenburg, Helge and Shalabh, (2001): A note on the comparison of minimax linear and mixed regression estimation of regression coefficients when prior estimates are available. Collaborative Research Center 386, Discussion Paper 238

Abstract

When prior estimates of regression coefficients along with their stan¡ dard errors or their variance covariance matrix are available, they can be incorporated into the estimation procedure through minimax linear and mixed regression approaches. It is demonstrated that the mixed regres¡ sion approach provides more efficient estimators, at least asymptotically, in comparison to the minimax linear approach with respect to the criterion of variance covariance matrix.

• Item Type: Paper (Research Paper)
• Keywords: linear regression; mixed model
• Subjects: Mathematics, Computer Science and Statistics; Mathematics, Computer Science and Statistics > Statistics; Mathematics, Computer Science and Statistics > Statistics > Collaborative Research Center 386
• Dewey Classification: 600 Natural sciences and mathematics; 600 Natural sciences and mathematics > 510 Mathematics