Minimax Estimator for linear models with nonrandom disturbances
AbstractThis paper collects some results of the authors on minimax estimation of parameters in linear (regression) models disturbed by some nuisance parameters. In contrast to conventional modelling, the disturbances are not specified as random variables but rather as unknown parameters, for which some a priori knowledge may be available. For various models and under different a priori restrictions on parameters and disturbances, either explicit formulas for the linear minimax estimator are derived or characterizations in form of spectral equations are obtained.
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Bibliographic InfoPaper provided by University of Bonn, Germany in its series Discussion Paper Serie B with number 359.
Date of creation: Jan 1995
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Find related papers by JEL classification:
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- E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Norbert Christopeit & Kurt Helmes, 1991. "On Minimax Estimation in Linear Regression Models with Ellipsoidal Constraints," Discussion Paper Serie B 205, University of Bonn, Germany.
- Bernhard Arnold & Peter Stahlecker, 2003. "Relative squared error prediction in the generalized linear regression model," Statistical Papers, Springer, vol. 44(1), pages 107-115, January.
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