The statistical classification of "N" individuals into "G" mutually exclusive groups when the actual group membership is unknown is common in the social and behavioral sciences. The results of such classification methods often have important consequences. Among the most common methods of statistical classification are linear discriminant analysis,…

The authors consider the problem of classifying an unknown observation into 1 of several populations by using tree-structured allocation rules. Although many parametric classification procedures are robust to certain assumption violations, there is need for classification procedures that can be used regardless of the group-conditional…

Accurate classification in discriminant analysis and the value of prediction are discussed, with emphasis on the uses and key aspects of prediction. A brief history of discriminant analysis is provided. C. J. Huberty's discussion of four aspects of discriminant analysis (separation, discrimination, estimation, and classification) is cited.…

We consider the problem of classifying an unknown observation into one of several populations using tree-structured allocation rules. Although many parametric classification procedures are robust to certain assumption violations, there is need for discriminant procedures that can be utilized regardless of the group-conditional distributions that…

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 Monte Carlo study evaluated the effectiveness of three rules of classifying objects into clusters: nearest neighbor classification; nearest centroid assignment; and quadratic discriminant analysis. Results suggest that the nearest neighbor rule is a useful tool for assessing the validity of the clustering procedure of J. H. Ward (1963). (SLD)

Three estimates of the probabilities of correct classification in predictive discriminant analysis were computed using mathematical formulas, resubstitution, and external analyses: (1) optimal hit rate; (2) actual hit rate; and (3) expected actual hit rate. Methods were compared using Monte Carlo sampling from two data sets. (Author/GDC)

This study compares the classification accuracy of linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), logistic regression (LR), and classification and regression trees (CART) under a variety of data conditions. Past research has generally found comparable performance of LDA and LR, with relatively less research on QDA and…

The power of the classical Linear Discriminant Function (LDF) is compared, using Monte Carlo techniques with five other procedures for classifying observations from certain non-normal distributions. The alternative procedures considered are the Quadratic Discriminant Function, a Nearest Neighbor Procedure with Probability Blocks, and three density…

Classification studies are important for practitioners who need to identify individuals for specialized treatment or intervention. When interventions are irreversible or misclassifications are costly, information about the proficiency of different classification procedures becomes invaluable. This study furnishes information about the relative…