Motivated by Markowitz portfolio optimization problems under uncertainty in the problem data, we consider general convex parametric multiobjective optimization problems under data uncertainty. For the first time, this uncertainty is treated by a robust multiobjective formulation in the gist of...

It is straightforward that breaking the <em>orthogonalitycondition</em> implies biased and inconsistent estimates by means of the<em> ordinary least squares</em>. If moreover, the data are contaminatedit may significantly worsen the data processing, even if it is performed by <em> instrumental variables</em> or the ...</em>

One of the approaches to compare forecasts is to test whether the loss from a benchmark prediction is smaller than the others. The test can be embedded into the general problem of testing functional inequalities using a one-sided Kolmogorov-Smirnov functional. This paper shows that such a test...