A lot of alternatives and constraints have been proposed in order to improve the Fisher criterion. But most of them are not linked to the error rate, the primary interest in many applications of classification. By introducing an upper bound for the error rate a criterion is developed which can...

Linear Discriminant Analysis (LDA) performs well for classifica- tion of business phases even though the premises of an LDA are not met. As the variables are highly correlated there are numerical as well as interpretational shortcomings. By transforming the classification problem to a regression...

A lot of alternatives and constraints have been proposed in order to improve the Fisher criterion. But most of them are not linked to the error rate, the primary interest in many applications of classification. By introducing an upper bound for the error rate a criterion is developed which can...

In this paper it is shown that the number of latent factors in a multiple multivariate regression model need not be larger than the number of the response variables in order to achieve an optimal prediction. The practical importance of this lemma is outlined and an application of such a...

Linear Discriminant Analysis (LDA) performs well for classifica- tion of business phases – even though the premises of an LDA are not met. As the variables are highly correlated there are numerical as well as interpretational shortcomings. By transforming the classification problem to a...

A lot of alternatives and constraints have been proposed in order to improve the Fisher criterion. But most of them are not linked to the error rate, the primary interest in many applications of classification. By introducing an upper bound for the error rate a criterion is developed which can...

In simulation studies Latent Factor Prediction Pursuit outperformed classical reduced rank regression methods. The algorithm described so far for Latent Factor Prediction Pursuit had two shortcomings: It was only implemented for situations where the explanatory variables were of full colum rank....

The optimization of the hyper-parameters of a statistical procedure or machine learning task is a crucial step for obtaining a minimal error. Unfortunately, the optimization of hyper-parameters usually requires many runs of the procedure and hence is very costly. A more detailed knowledge of the...

In simulation studies Latent Factor Prediction Pursuit outperformed classical reduced rank regression methods. The algorithm described so far for Latent Factor Prediction Pursuit had two shortcomings: It was only implemented for situations where the explanatory variables were of full colum rank....

The optimization of the hyper-parameters of a statistical procedure or machine learning task is a crucial step for obtaining a minimal error. Unfortunately, the optimization of hyper-parameters usually requires many runs of the procedure and hence is very costly. A more detailed knowledge of the...