Titlebook: Building Bridges between Soft and Statistical Methodologies for Data Science; Luis A. García-Escudero,Alfonso Gordaliza,Olgierd Conferenc

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https://doi.org/10.1007/978-3-7091-1574-9 of the estimators, while arbitrary decisions about their value could severely hamper inferential results. To guide the user in the delicate choice of such parameters, a monitoring approach has been introduced in the recent literature, yielding an adaptive method. The approach is here exemplified, v

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https://doi.org/10.1007/978-3-319-71538-4onal challenges, as some restrictions, imposed by the model and their estimates, are required. The method is illustrated by an application to real data where exports of coffee to European Union are analyzed to identify outliers that might be linked to potential frauds. An additional short simulation

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Maternal Distress and Postnatal Depressionking with E-admissibility. This means that from any finite set of options, we reject those options that no probability mass function compatible with the given information gives the highest expected utility. We use the mathematical framework of choice functions to specify choices and rejections, and

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https://doi.org/10.1007/978-3-030-17898-7tructures for each occasion and returns a common low-dimensional space which is representative for all occasions. Finally, a real application to three-way data is presented to illustrate the potential of the method.

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https://doi.org/10.1007/978-3-030-17898-7se sets is very useful to identify their original shape. The complexity of these data lies in the fact that several aspects of the data are considered in a single representation. Since in some situations it may be very useful to identify groups of these sets, a fuzzy clustering method has been propo