Browsing Faculty of Natural and Agricultural Sciences by Subject "non-parametric"
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(Taylor & Francis, 2012)We propose a sequential cumulative sum procedure to detect deviations from uniformity in angular data. The method is motivated by a problem in high-energy astrophysics and is illustrated by an application to data.
Variable selection for binary classification using error rate p-values applied to metabolomics data (BioMed Central, 2016)Background: Metabolomics datasets are often high-dimensional though only a limited number of variables are expected to be informative given a specific research question. The important task of selecting informative variables ...