Genetic algorithm based outlier detection using information criterion

Outlier, abnormal or unusual observation can be defined as an observation that lies outside the overall pattern of a distribution. Diagnostic methods for identifying a single outlier or influential observation in a linear regression model are relatively simple from both analytical and computational points of view. However, if the data set contains more than one outlier, which is likely to be the case in most data sets, the problem of identifying such observations becomes more difficult because of the masking and swamping effects.In this thesis, Genetic Algorithm (GA) based outlier detection using information criteria in multiple regression models has been studied. A GA was allowed simultaneous detection of outliers in data sets. Thus, this method is to overcome the problems of masking and swamping effects. It is derived additional penalized value of information criteria for Akaike Information Criterion (AIC) and Information Complexity Criterion (ICOMP) and named as AIC' and ICOMP' respectively in this study. They have been used as the fitness function of genetic algorithms to detect outliers in multiple regression. The simulation study has been performed to compare consistency and robustness properties of AIC' and ICOMP' against corrected Bayesian Information Criterion (BIC'). Simulation results of AIC', BIC' and ICOMP' obtained from different number of sample sizes, different penalized Kappa values of information criterion and different number of explanatory variables for different percentage of outlier in dependent variables. The numerical example and simulation results clearly show a much improved performance of the proposed approach in comparison to existing method especially followed by applying the ICOMP' approach in order to accurately (robustly) detect the outliers.