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We consider the linear regression model where the parameter space is restricted by linear inequalities. The effect of projecting estimators that may violate this polyhedral prior information is analyzed. We show that projected estimators dominate their unprojected counterparts. The magnitude of the perform- ance gain is investigated in a Monte Carlo Study.
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Schmidt et al. (1995) studied this question.
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