In the context of binary classification with continuous predictors, we proove two properties concerning the connections between Partial Least Squares (PLS) dimension reduction and between-group PCA, and between linear discriminant analysis and between-group PCA. Such methods are of great...

The combination of classifiers leads to substantial reduction of misclassification error in a wide range of applications and benchmark problems. We suggest to use the out-of-bag sample for combining different classifiers. In our setup, a Linear Discriminant Analysis is performed using the...

The classical approach to testing for structural change employs retrospective tests using a historical data set of a given length. Here we consider a wide array of fluctuation-type tests in a monitoring situation – given a history period for which a regression relationship is known to be...

The paper presents an approach to the analysis of data that contains (multiple) structural changes in a linear regression setup. We implement various strategies which have been suggested in the literature for testing against structural changes as well as a dynamic programming algorithm for the...

The paper presents an approach to the analysis of data that contains (multiple) structural changes in a linear regression setup. We implement various strategies which have been suggested in the literature for testing against structural changes as well as a dynamic programming algorithm for the...

Non-homogeneous regression is often used to statistically post-process ensemble forecasts. Usually only ensemble forecasts of the predictand variable are used as input but other potentially useful information sources are ignored. Although it is straightforward to add further input variables,...

The classical approach to testing for structural change employs retrospective tests using a historical data set of a given length. Here we consider a wide array of fluctuation-type tests in a monitoring situation – given a history period for which a regression relationship is known to be...