Ordering algorithms and confidence intervals in the presence of nuisance parameters

We discuss some issues arising in the evaluation of confidence intervals in the presence of nuisance parameters (systematic uncertainties) by means of direct Neyman construction in multi-dimensional space. While this kind of procedure provides rigorous coverage, it may be affected by large overcoverage, and/or produce results with counterintuitive behavior with respect to the uncertainty on the nuisance parameters, or other undesirable properties. We describe a choice of ordering algorithm that provides results with good general properties, the correct behavior for small uncertainties, and limited overcoverage.