Linearized Rank Estimates and Signed-Rank Estimates for the General Linear Hypothesis

Abstract

Linearized estimates, as functions of the ranks, are proposed for the general linear hypothesis. These estimates can be computed after a single ranking of the "centered" observations. The asymptotic distribution of the estimates is shown to be the same as the maximum likelihood estimates for fairly general sequences of design matrices.