Studentized robust statistics in multivariate randomized block design

Multiresponse experiments in randomized block design are considered. Statistical procedures of the test and estimation, based on studentized robust statistics, for location parameters in the models are proposed. Large sample properties of their procedures as the number of blocks tends to infinity are investigated. Although Fisher's consistency is assumed in the theory of M-estimators, it is not needed in this paper. For the univariate case, it is found that the asymptotic relative efficiencies (ARE's) of the proposed procedures relative to classical procedures goes to the classical ARE-results of Huber's one sample M-estimators relative to the sample mean as block size goes to infinity. By simulation studies, it can be seen that the proposed estimators are more efficient than least squares estimators except for the case where the underlying distribution is normal.