Titlebook: Statistical Learning and Modeling in Data Analysis; Methods and Applicat Simona Balzano,Giovanni C. Porzio,Maurizio Vichi Conference procee

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ACE, AVAS and Robust Data Transformations,ter values. The procedure is illustrated for data with many outliers. The data are cleaned with a robust method, the forward search, and the obtained transformations compared with the results from two nonparametric transformation methods based on data smoothing.

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Effect Measures for Group Comparisons in a Two-Component Mixture Model: A Cyber Risk Analysis,ed measures for comparing clusters on ratings, while adjusting for other explanatory variables, and discuss marginal effects to address the interpretation of the results on the extreme categories of a cyber risk scale.

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On Mean And/or Variance Mixtures of Normal Distributions,ean and/or covariance matrix of the (multivariate) normal distribution with some scaling variable(s). Namely, we consider the families of the mean mixture, variance mixture, and mean–variance mixture of normal distributions. Their basic properties, some notable special/limiting cases, and parameter estimation methods are also described.

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On Predicting Principal Components Through Linear Mixed Models,ork offers a comprehensive baseline to get a dimensionality reduction of a variety of random-effects modeled data. Alongside the suitability of using model covariates and specific covariance structures, the method allows the researcher to assess the crucial changes of a set of multivariate vectors f

Ischemia

Robust Model-Based Learning to Discover New Wheat Varieties and Discriminate Adulterated Kernels inearning set could be unreliable (label noise). Further, a proportion of observations might depart from the main structure of the data (outliers) and new groups may appear in the test set, which were not encountered earlier in the training phase (unobserved classes). Therefore, we present here a robu