Physical Sciences and Mathematics Commons^™

Identifying Outliers In A Random Effects Model For Longitudinal Data, Tamarah Crouse Dishman

UNF Graduate Theses and Dissertations

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 …