In this article we extend and implement the forward search algorithm for identifying atypical subjects/observations in factor analysis models. The forward search has been mainly developed for detecting aberrant observations in regression models (Atkinson, 1994) and in multivariate methods such as cluster and discriminant analysis (Atkinson, Riani,…

This article proposes an intuitive approach for predictive discriminant analysis with mixed continuous, dichotomous, and ordered categorical variables that are defined via an underlying multivariate normal distribution with a threshold specification. The classification rule is based on the comparison of the observed data logarithm probability…

A two-group classification procedure for multivariate binary data is presented and discussed for the case of independent samples. An empirical example is provided. (Author/JKS)

Two data sets are cluster-analyzed by the minimum variance procedure, and centroid vectors for the first are calculated. The objects of the second are assigned to the nearest centroid calculated in the first data set, and the results are evaluated in terms of stability and accuracy. (Author/BW)

The shape of the response surface associated with a discriminant analysis provides insight into the value of the derived optimal discriminant variates. A procedure for the determination of "indifference regions," presented in this article, allows the assessment of the degree of flatness of the response surface for any analysis.…

The use of generalized discriminant analysis as a descriptive technique which can be employed outside of the traditional analysis of variance studies is discussed. Examples based on real data are provided. (Author/JKS)

A Statistical Analysis System (SAS) macro procedure for performing a jackknife analysis on structure coefficients in discriminant analysis is described together with issues and caveats about its use in multivariate methods. An example of use of the SAS macro is provided. (SLD)

Classification is a procedure through which individuals are classified as being members of a particular group based on a variety of independent variables. Two methods of makin such classifications are discussed; the quadratic method is seen to be superior to the linear under certain constraints. (JKS)

The revisions applied to the linear discriminant functions (LDF's) for computerized psychiatric diagnosis are discussed in terms of a comparison of the LDF model with decision tree models. It is concluded that each model has its own advantages and disadvantages. (Author/DEP)

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…

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)

Suppose P to the subpower of 1 and P to the subpower of 2 are two competing discrimination procedures. To compare the relative discriminatory power of both procedures, test statistics are suggested for the hypothesis that P to the subpower of 1 and P to the subpower of 2 performed no better than random assignment versus the alternative that P to…

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)

Confirmatory factor analysis was applied to test second- and higher-order factor models in the areas of structure of abilities, allometry, and the separation of specific and error variance estimates. The estimation of validity and reliability, second-order models within factor analysis models, and the concept of discriminability were also studied.…