Analysis of Variance for Item Differences in Verification Data with Unknown Groups

For sequentially collected data, this paper introduces a lag-one differencing method to estimate the random error standard deviation and then uses the estimate to calculate a change detection threshold in a moving window method to detect shifts in the short-term systematic error. Performance results on simulated and real data are presented. Fortunately, the impact of having to perform change detection on the estimated short-term systematic and random error variances is anticipated to be modest or small. The motivating example arises from facilities under nuclear safeguards agreements, where inspector data collected during International Atomic Energy Agency (IAEA) verifications are compared to corresponding operator data. The differences between the operator and inspector values are evaluated using an application of analysis of variance (ANOVA). Typically, it is assumed that short-term systematic errors change across inspection periods, so inspection periods form the groups used in the ANOVA. In some data sets, it appears that the short-term errors have changed at other times, so change detection methods could be used to detect the actual change times.

from ! Human Diseases via Alexandros G.Sfakianakis on Inoreader http://bit.ly/2EYvhjT