A linear regression function is developed for use in a classification procedure. The procedure is applied to faculty merit review data, resulting in an interpretable regression function and within-sample classifications as good as a four-funtion discriminant analysis. (Author/BW)

The coincidence hypothesis predicts that dissimilarity between objects that differ on two separable dimensions is larger than predicted from their unidimensional differences on the basis of triangle inequality and segmental additivity. The coincidence hypothesis was supported in two-dimensional stimuli studies. (Author/CM)

A method for robust canonical discriminant analysis via two robust objective loss functions is discussed. Majorization is used at several stages in the minimization procedure to obtain a monotonically convergent algorithm. A simulation study and empirical data illustrate the procedure. (SLD)

Objective: The aim of this study is twofold: First, to investigate whether cognitive functions can contribute to differentiating neglected children with or without physical abuse compared to comparison participants; second, to demonstrate the detrimental impact of children being victimized by a combination of different types of maltreatment.…

Purpose: To evaluate the discriminant accuracy of a grammatical measure for the identification of language impairment (LI) in Latino English-speaking children. Specifically, the study examined the diagnostic accuracy of the Test of English Morphosyntax (E-MST; Pena, Gutierrez-Clellen, Iglesias, Goldstein, & Bedore (n.d.) to determine (a)…

Methodological issues of using a class of neural networks called Mixture Density Networks (MDN) for discriminant analysis are discussed. MDN models have the advantage of having a rigorous probabilistic interpretation, and they have proven to be a viable alternative as a classification procedure in discrete domains. Both classification and…

Either linear or quadratic rules may be used to derive classification equations in discriminant analysis for the purpose of predicting group membership. Generally, the decision about which rule to use is governed by the degree to which the separate group covariance matrices are unequal. An example is presented that supports the superior internal…

To assess the effect on discriminant analysis in terms of correct classification into two groups, the following parameters were systematically altered using Monte Carlo techniques: sample sizes; proportions of one group to the other; number of independent variables; and covariance matrices. The pairing of the off diagonals (or covariances) with…

A series of experiments was performed to investigate the effectiveness and utility of automatically classifying documents through the use of multiple discriminant functions. Classification is accomplished by computing the distance from the mean vector of each category to the vector of observed frequencies of a document and assigning the document…

Three methods of transforming unordered categorical response variables are described: (1) analysis using dummy variables; (2) eigenanalysis of frequency patterns scaled relative to within-groups variance; (3) categorical variables analyzed separately with scale values generated so that the grouping variable and the categorical variable are…

Investigated the ability of the Wechsler Scale for Children-Revised (WISC-R) subtest scores to distinguish between three groups of behaviorally disordered boys and a control group. Results suggest that although WISC-R subtest scores were able to distinguish between the groups, their use as a classification metric could be highly misleading.…

Mathematical programing formulas are introduced as new approaches to solve the classification problem in discriminant analysis. The research literature is reviewed, and an illustration using a real-world classification problem is provided. Issues relevant to potential uses of these formulations are discussed. (TJH)

A method for comparing the cross validated classification accuracies of linear and quadratic classification rules is presented under varying data conditions for the k-group classification problem. With this method, separate-group as well as total-group proportions of correct classifications can be compared for the two rules. McNemar's test for…