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- W77790004 abstract "Publisher Summary This chapter discusses one particular aspect of the robustness of the linear discriminant function, that is, robustness against contamination of the initial samples. Very little research has been done on the robustness of the linear discriminant function. The linear discriminant function is used to assign observations to one of two populations. If the underlying distributions of the observations are multivariate normal with the same covariance matrices, the sample discriminant has certain desirable asymptotic properties. If the underlying populations are not normal, the discriminant still has some desirable properties, but is not, in general, the optimal rule in any sense. The amount of loss of discriminating power is a function of how non-normal the distributions are. It is known, however, that even slight non-normality can hurt location estimators considerably. The overall probability of misclassification can be greatly affected by location contamination, depending upon the direction of the contamination. The greatest effect of location contamination occurs when the contaminating mean is on the opposite side of the uncontaminated population mean." @default.
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- W77790004 date "1977-01-01" @default.
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- W77790004 title "Discriminant Analysis When Scale Contamination Is Present in the Initial Sample" @default.
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- W77790004 doi "https://doi.org/10.1016/b978-0-12-714250-0.50016-4" @default.
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