College of Arts and Sciences
Master of Science in Mathematical Sciences (MS)
Mathematics & Statistics
Dr. Donna Mohr
Dr. Pali Sen
Identifying non-tracking individuals in a population of longitudinal data has many applications as well as complications. The analysis of longitudinal data is a special study in itself. There are several accepted methods, of those we chose a two-stage random effects model coupled with the Estimation Maximization Algorithm (E-M Algorithm) . Our project consisted of first estimating population parameters using the previously mentioned methods. The Mahalanobis distance was then used to sequentially identify and eliminate non-trackers from the population. Computer simulations were run in order to measure the algorithm's effectiveness.
Our results show that the average specificity for the repetitions for each simulation remained at the 99% level. The sensitivity was best when only a single non-tracker was present with a very different parameter a. The sensitivity of the program decreased when more than one tracker was present, indicating our method of identifying a non-tracker is not effective when the estimates of the population parameters are contaminated.
Dishman, Tamarah Crouse, "Identifying Outliers in a Random Effects Model For Longitudinal Data" (1989). UNF Graduate Theses and Dissertations. 191.