We describe a computer intensive method for linear dimension reduction which minimizes the classification error directly. Simulated annealing Bohachevsky et al (1986) is used to solve this problem. The classification error is determined by an exact integration. We avoid distance or scatter...

In this paper, control variates are proposed to speed up Monte Carlo Simulations to estimate expected error rates in multivariate classification.

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 multivariate classification as a statistical tool to describe business cycles. These cycles are often analyzed as a univariate phenomenon in terms of GNP or industrial net production ignoring additional information in other economic variables. Multivariate classification overcomes...

This paper illustrates the Support Vector Method for the classification problem with two and more classes. In particular, the multi-class classification Support Vector Method of Weston and Watkins (1998) is correctly formulated as a quadratic optimization problem. Then, the method is applied to...

Thirteen Stylized Facts of the german economy are studied with different descriptive statistical methods. The results of this study are considered with respect to other results from Project B3 Multivariate Bestimmung und Untersuchung von Konjunkturzyklen.

The properties of Cpmk in the presence of asymmetric specification limits are discussed. It is shown that Cpmk tends to zero as the process variation increases and vice versa. Furthermore, if the process variation is small, Cpmk has its maximum near the target value but the maximum moves towards...

In this paper, we examine the German business cycle (from 1955 to 1994) in order to identify univariate and multivariate outliers as well as influence points corresponding to Linear Discriminant Analysis. The locations of the corresponding observations are compared and economically interpreted.