When evaluating differences between competing precipitation forecasts, formal hypothesis testing is rarely performed. This may be due to the difficulty in applying common tests given the spatial correlation of and nonnormality of errors. Possible ways around these difficulties are explored here. Two datasets of precipitation forecasts are evaluated, a set of two competing gridded precipitation ...

In this paper, we compare three different generalization methods for in-domain and cross-domain opinion holder extraction being simple unsupervised word clustering, an induction method inspired by distant supervision and the usage of lexical resources. The generalization methods are incorporated into diverse classifiers. We show that generalization causes significant improvements and that the i...

We give an overview of the basic principles of approximate Bayesian computation (ABC), a class of stochastic methods that enable flexible and likelihood-free model comparison and parameter estimation. Our new open-source software called ABrox is used to illustrate ABC for model comparison on two prominent statistical tests, the two-sample t-test and the Levene-Test. We further highlight the fle...

In large-scale genetic studies, a primary aim is to test for an association between genetic variants and a disease outcome. The variants of interest are often rare and appear with low frequency among subjects. In this situation, statistical tests based on standard asymptotic results do not adequately control the type I error rate, especially if the case : control ratio is unbalanced. In this ar...