Multivariate statistics: a practical approach
Multivariate statistics: a practical approach
Detecting influential observations for estimated probabilities in multiple discriminant analysis
Computational Statistics & Data Analysis
High breakdown estimation for multiple populations with applications to discriminant analysis
Journal of Multivariate Analysis
Influence function and efficiency of the minimum covariance determinant scatter matrix estimator
Journal of Multivariate Analysis
Journal of Multivariate Analysis
Robustness of classification rules that incorporate additional information
Computational Statistics & Data Analysis
Robust discrimination under a hierarchy on the scatter matrices
Journal of Multivariate Analysis
Projection-pursuit approach to robust linear discriminant analysis
Journal of Multivariate Analysis
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In this paper it is studied how observations in the training sample affect the misclassification probability of a quadratic discriminant rule. An approach based on partial influence functions is followed. It allows to quantify the effect of observations in the training sample on the performance of the associated classification rule. Focus is on the effect of outliers on the misclassification rate, merely than on the estimates of the parameters of the quadratic discriminant rule. The expression for the partial influence function is then used to construct a diagnostic tool for detecting influential observations. Applications on real data sets are provided.