title: Outlier detection method in GEEs.
abstract: The generalized estimating equations (GEEs) method has become quite useful in modeling correlated data. However, diagnostic tools to check that the selected final model fits the data as accurately as possible have not been explored intensively. In this paper, an outlier detection technique is developed based on the use of the "working" score test statistic to test an appropriate mean-shift model in the context of longitudinal studies based on GEEs. Through a simulation study it has been shown that this method correctly singled out the outlier when the data set had a known outlier. The method is applied to a set of data to illustrate the outlier detection procedure in GEEs.
authors.affiliation: Department of Statistics and O.R. (I), Faculty of Mathematics, Complutense University of Madrid, E-28040, Madrid, Spain.
authors.forename: María Del Carmen
authors.initials: Mdel C
authors.lastname: Pardo
authors.affiliation: Department of Mathematics, Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University in Prague, Trojanova 13, 120 00 Prague 2, Czech Republic.
authors.forename: Tomáš
authors.initials: T
authors.lastname: Hobza
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D008137:Longitudinal Studies; D008962:Models, Theoretical
keywords: Generalized estimating equations (GEEs); Longitudinal data; Outlier detection; Score test statistic