When trying to interpret estimated parameters the researcher is interested in the (relative) importance of the individual predictors. However, if the predictors are highly correlated, the interpretation of coefficients, e.g. as economic multipliersʺ, is not applicable in standard regression or...

We propose a computer intensive method for linear dimension reduction which minimizes the classification error directly. Simulated annealing (Bohachevsky et al 1986) as a modern optimization technique is used to solve this problem effectively. This approach easily allows to incorporate user...

We propose a standardized partition space (SPS) that offers a unifying framework for the comparison of a wide variety of classification rules. Using SPS, one can define measures for the performance of classifiers w.r.t. goodness concepts beyond the expected rate of correct classifications of the...

We consider a unified description of classification rules for nearly singular covariance matrices. When the covariance matrices of the groups or the pooled covariance matrix become nearly singular, bayesian classification rules become seriously unstable. Several procedures have been proposed to...

When trying to interpret estimated parameters the researcher is interested in the (relative) importance of the individual predictors. However, if the predictors are highly correlated, the interpretation of coefficients, e.g. as economic ?multipliers?, is not applicable in standard regression or...