In this paper we show that for the D-optimal design, departures from the design are much less important than a departure from the model. As a consequence, we propose, based on D-optimality, a rule for choosing the regression degree. We also study different types of departures from the model to define a new class of D-optimal designs, which is robust and more efficient than the uniform one.
In this article we present a technique of data analysis applied to three-dimensional tables as, for instance, matrix-valued time-series. The main goal of the method is to describe the evolution of the statistical units with respect to time in a space summarizing the set of matrices. Moreover, our technique points out similar statistical units provided by a classification of their trajectories.