Ivana Pavlů, Alessandra Menafoglio, E.G. Bongiorno, Karel Hron
The process of supervised classifcation when the data set consists of probability density functions is studied. Due to the relative information contained in densities, it is ne- cessary to convert the functional data analysis methods into an appropriate framework, here represented by the Bayes spaces. This work develops Bayes space counterparts to a set of commonly used functional methods with a focus on classifcation. Hereby, a clear guideline is provided on how some classifcation approaches can be adapted for the case of densities. Comparison of the methods is based on simulation studies and real-world applications, refecting their respective strengths and weaknesses
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