caa a22 4500 477076556 CHVBK 20180405111440.0 cr unu---uuuuu 170330e19960401xx s 000 0 eng 10.1007/BF00046990 doi (NATIONALLICENCE)springer-10.1007/BF00046990 Hoffmann Kurt Ludwig-Renn-Strasse 37, 12679, Berlin, Germany aut A subclass of bayes linear estimators that are minimax [Elektronische Daten] [Kurt Hoffmann] In the linear regression model with ellipsoidal parameter constraints, the problem of estimating the unknown parameter vector is studied. A well-described subclass of Bayes linear estimators is proposed in the paper. It is shown that for each member of this subclass, a generalized quadratic risk function exists so that the estimator is minimax. Moreover, some of the proposed Bayes linear estimators are admissible with respect to all possible generalized quadratic risks. Also, a necessary and sufficient condition is given to ensure that the considered Bayes linear estimator improves the least squares estimator over the whole ellipsoid whatever generalized risk function is chosen. Kluwer Academic Publishers, 1996 linear regression nationallicence Bayes linear estimation nationallicence restricted parameter space nationallicence minimax property nationallicence Acta Applicandae Mathematica Kluwer Academic Publishers 43/1(1996-04-01), 87-95 0167-8019 43:1<87 1996 43 10440 https://doi.org/10.1007/BF00046990 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/BF00046990 text/html Onlinezugriff via DOI NATIONALLICENCE 100 1- Hoffmann Kurt Ludwig-Renn-Strasse 37, 12679, Berlin, Germany aut NATIONALLICENCE 773 0- Acta Applicandae Mathematica Kluwer Academic Publishers 43/1(1996-04-01), 87-95 0167-8019 43:1<87 1996 43 10440 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer