Rank-based inference methods are applied in various disciplines, typically when procedures relying on standard normal theory are not justifiable. Various specific rank-based methods have been developed for two and more samples and also for general factorial designs (e.g. Kruskal–Wallis test or...

Hyperparameter tuning is one of the most time-consuming parts in machine learning. Despite the existence of modern optimization algorithms that minimize the number of evaluations needed, evaluations of a single setting may still be expensive. Usually a resampling technique is used, where the...

Preparing people for dealing with hazards, diseases and disasters requires teaching them statistics, and ideally doing so by means of good representation formats in a dynamic fashion. Translating these dynamics to simple communication is what governments need from scientists.

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...