Robust regression and outlier detection
Robust regression and outlier detection
Robust two-group discrimination by bounded influence regression: a Monte Carlo simulation
Computational Statistics & Data Analysis
High breakdown estimation for multiple populations with applications to discriminant analysis
Journal of Multivariate Analysis
High breakdown estimators for principal components: the projection-pursuit approach revisited
Journal of Multivariate Analysis
Influence of observations on the misclassification probability in quadratic discriminant analysis
Journal of Multivariate Analysis
Random Projection with Robust Linear Discriminant Analysis Model in Face Recognition
CGIV '07 Proceedings of the Computer Graphics, Imaging and Visualisation
A Projection Pursuit Algorithm for Exploratory Data Analysis
IEEE Transactions on Computers
Computational Statistics & Data Analysis
A new discriminant analysis approach under decision-theoretic rough sets
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Incorporating logistic regression to decision-theoretic rough sets for classifications
International Journal of Approximate Reasoning
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Discriminant analysis plays an important role in multivariate statistics as a prediction and classification method. It has been successfully applied in many fields of work and research. As it happens with other multivariate methods, discriminant analysis is highly vulnerable to the presence of outliers that commonly occur in many real world data sets. The lack of robustness of the classical estimators on which the linear discriminant function is based is a severe disadvantage and several authors have worked to find efficient ways to prevent the damage that outliers can cause. This paper focuses on the projection-pursuit approach to discriminant analysis. The projection-pursuit estimators are described and theoretical properties are deduced and their relevance is highlighted. These include Fisher consistency, affine equivariance, partial influence functions and asymptotic distributions. Application to real data and a simulation study reveal the robustness of the projection-pursuit approach. In both analyses the data relates to a large number of variables, a situation that is becoming common when new technology is applied to data gathering.