The objective of this study was to evaluate the ability of a new technique to identify systematic measurement errors (i.e. biases) in process variables. This technique obtains high identification accuracy and computational speed by efficiently selecting a small subset of statistical hypothesis tests from a very large set using new selection criteria developed in this work. In this article the proposed technique is also 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 decreased, significantly more accurate in identifying systematic errors.
D. K. Rollins, Cheng, Y., and Dr. Sriram Devanathan, “Intelligent selection of hypothesis tests to enhance gross error identification”, Computers & chemical engineering, vol. 20, pp. 517–530, 1996.