The papers in this book cover issues related to the development of novel statistical models for the analysis of data. They offer solutions for relevant problems in statistical data analysis and contain the explicit derivation of the proposed models as well as their implementation. The book assembles the selected and refereed proceedings of the biannual conference of the Italian Classification and Data Analysis Group (CLADAG), a section of the Italian Statistical Society.

Presents novel research in the field of statistical models for data analysis Offers statistical solutions for relevant problems Contains explicit derivation of the proposed models as well as their implementation? Includes supplementary material: sn.pub/extras

Inhalt

From the contents: Ordering curves by data depth.- May the students' career performance helpful in predicting an increase in universities income?.- Model-based classification via patterned covariance analysis.- Data stream summarization by histograms clustering.- Web panel representativeness.- Nonparametric inference via permutation tests for CUB models.- Asymmetric multidimensional scaling models for seriations.- An approach to ranking the hedge funds industry.- Correction of incoherences in statistical matching.- The analysis of network additionality in the context of territorial innovation policy.- Clustering and registration of multidimensional functional data.- Classifying tourism destinations.- On two classes of weighted rank correlation measures deriving from teh Spearman's r.- Beanplot data analysis in a temporal framework.- Supervised classification of facial expressions.- Grouping around different dimensional affine subspaces.- Hospital clustering in the treatment of acute myocardial infarction patients via a Bayesian semiparametric approach.- A new fuzzy method to classify professional profiles from job announcements.- A metric based approach for the Least Square Regression of multivariate model symbolic data.- A Gaussian-Von Mises Hidden Markov model for clustering multivariate linear-circular data.- And further articles.