Author:

Keywords:

Applications, Classification, Data, Diagnostics, Discriminant analysis, Estimator, Functions, Influence function, Misclassification probability, Outliers, Parameters, Partial influence functions, Performance, Principal components, Probability, Quadratic discriminant analysis, Tool, Training, Science & Technology, Physical Sciences, Statistics & Probability, Mathematics, classification, diagnostics, misclassification probability, outliers, partial influence functions, quadratic discriminant analysis, COVARIANCE, MATRIX, 0104 Statistics, 1403 Econometrics, 3802 Econometrics, 4905 Statistics

Abstract:

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.