We consider a multivariate ultrastructural measurement error model when some information on regression coefficients is available in the form of linear restriction. We propose Stein-rule estimation for the regression coefficients. Without any assumption about the distribution for the random components, the asymptotic risk under a specific quadratic loss function is studied and a sufficient condition for the dominance of the proposed estimator over a consistent estimator. We also carry out a simulation study to demonstrate the finite sample properties of the estimators.
