When analyzing business cycle data, one observes that the relevant predictor variables are often highly correlated. This paper presents a method to obtain measures of importance for the classification of data in which such multicollinearity is present. In systems with highly correlated variables...

Aiming at optimal prediction of the correct note corresponding to a vocal time series we trained a classification algorithm on the basis of parts of interpretations of Tochter Zion (Händel) and tested the algorithm on the remaining parts. As classification algorithm we use a radial basis...

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.

We present a descriptive analysis of stylized facts for the German business cycle. We demonstrate that simple ad-hoc instructions for identifying univariate rules characterizing the German business cycle 1955-1994 lead to an error rate comparable to standard multivariate methods.

When analyzing business cycle data, one observes that the relevant predictor variables are often highly correlated. This paper presents a method to obtain measures of importance for the classification of data in which such multicollinearity is present. In systems with highly correlated variables...

Aiming at optimal prediction of the correct note corresponding to a vocal time series we trained a classification algorithm on the basis of parts of interpretations of Tochter Zion (Händel) and tested the algorithm on the remaining parts. As classification algorithm we use a radial basis...

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.