Biased estimation in a simple multivariate regression model

Abstract Estimation of the intercept vector in a simple multivariate normal regression model is considered, when it is a priori suspected that the slope vector may be restricted to a subspace. We propose two new Stein-type and preliminary test estimators for the parameter vector. The positive-part estimator is superior to the usual Stein-type estimator, and the proposed preliminary test estimator outperforms the standard preliminary test estimator regardless of the correctness of the nonsample information. The relative dominance picture of estimators is presented.

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