On the Uniqueness and Globality of Optimal Data Gathering Strategies

It was shown in a previous publication that, in order to minimize the prediction error of a statistical model with a discrete dependent variable, it is possible to design an optimal survey by solving a nonlinear mathematical program with linear constraints. It is shown here that this mathematical program is a convex programming problem and that the gradient and Hessian of the objective function admit a closed form. Consequently, very efficient optimal seeking methods which guarantee a globally optimal sampling strategy can be used. As a byproduct of the analysis a simple test for multicollinearity was also developed.