caa a22 4500 477076629 CHVBK 20180405111440.0 cr unu---uuuuu 170330e19960401xx s 000 0 eng 10.1007/BF00046993 doi (NATIONALLICENCE)springer-10.1007/BF00046993 Schmidt Karsten Department of Economics, FH Schmalkalden, Germany aut A comparison of minimax and least squares estimators in linear regression with polyhedral prior information [Elektronische Daten] [Karsten Schmidt] We consider the linear regression model where prior information in the form of linear inequalities restricts the parameter space to a polyhedron. Since the linear minimax estimator has, in general, to be determined numerically, it was proposed to minimize an upper bound of the maximum risk instead. The resulting so-called quasiminimax estimator can be easily calculated in closed form. Unfortunately, both minimax estimators may violate the prior information. Therefore, we consider projection estimators which are obtained by projecting the estimate in an optional second step. The performance of these estimators is investigated in a Monte Carlo study together with several least squares estimators, including the inequality restricted least squares estimator. It turns out that both the projected and the unprojected quasiminimax estimators have the best average performance. Kluwer Academic Publishers, 1996 parameter restrictions nationallicence inequality restricted least squares estimator nationallicence minimax estimation nationallicence projection estimators nationallicence average performance nationallicence Acta Applicandae Mathematica Kluwer Academic Publishers 43/1(1996-04-01), 127-138 0167-8019 43:1<127 1996 43 10440 https://doi.org/10.1007/BF00046993 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/BF00046993 text/html Onlinezugriff via DOI NATIONALLICENCE 100 1- Schmidt Karsten Department of Economics, FH Schmalkalden, Germany aut NATIONALLICENCE 773 0- Acta Applicandae Mathematica Kluwer Academic Publishers 43/1(1996-04-01), 127-138 0167-8019 43:1<127 1996 43 10440 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer