INTELLIGENT SELECTION OF HYPOTHESIS TESTS TO ENHANCE GROSS ERROR IDENTIFICATION

The objective of this study was to evaluate the ability of a new techn ique to identify systematic measurement errors (i.e. biases) in proces s variables. This technique obtains high identification accuracy and c omputational speed by efficiently selecting a small subset of statisti cal hypothesis tests from a very large set using new selection criteri a developed in this work. In this article the proposed technique is al so evaluated and compared to a well known method in a fairly extenisve Monte Carlo simulation study. The proposed technique was found to be computationally faster and, as the variances of measurement errors dec reased, significantly more accurate in identifying systematic errors.