Statistical Models and Methods for Data Science

Clustering financial time series by dependency.- The Homogeneity Index as a Measure of Interrater Agreement for Ratings on a Nominal Scale.- Hierarchical clustering of income data based on share densities.- Optimal Coding of High Cardinality Categorical Data in Machine Learning.- Bayesian Multivariate Analysis of Mixed data.- Marginals matrix under a generalized Mallows model based on the power divergence.- Time series clustering based on forecast distributions: an empirical analysis on production indices for construction.- Partial Reconstruction of Measures from Halfspace Depth.- Posterior Predictive Assessment of IRT Models via the Hellinger Distance: A Simulation Study.- Shapley Lorenz values for credit risk management.- A study of lack-of-fit diagnostics for models fit to cross-classified binary variables.- Robust Response Transformations for Generalized Additive Models via Additivity and Variance Stabilisation.- A Random-Coefficients Analysis with a Multivariate Random-Coefficients Linear Model.- Parsimonious mixtures of matrix-variate shifted exponential normal distributions.

Leonardo Grilli is a Full Professor of Statistics at the University of Florence, Italy and the Director of the Master's program in Statistics and Data Science and a member of the board of the Doctoral Program in Development Economics and Local Systems. His teaching activity focuses on introductory statistics and statistical modelling, including generalized linear models and multilevel models. His research mainly concerns random effects models for multilevel analysis, with methodological advances about the specification and estimation of models in complex frameworks such as duration data, multivariate qualitative responses, informative sampling designs, and sample selection bias. He has also made contributions to causal inference in the potential outcomes framework, IRT models, latent growth curve models, mixture models, and quantile regression.

Monia Lupparelli is an Associate Professor of Statistics at the University of Florence, Italy, and a member of the boardof the Doctoral Program in Statistics at the University of Bologna. She teaches the foundations of statistics and of statistical modelling in bachelor's and master's degree programs. She also gives specialised courses on graphical models and categorical data analysis for doctoral and post-doctoral students.  Her research activity is mainly grounded in network modelling, latent variable models, and marginal models with applications to the analysis of longitudinal and categorical data in social and biomedical sciences.

Carla Rampichini is a Full Professor of Statistics and Head of the Department of Statistics, Computer science, and Applications at the University of Florence, Italy. Her teaching activity focuses on introductory statistics, multivariate analysis and statistical modelling. Her research interests relate to random effects models for multilevel analysis, program evaluation, and causal inference. Her methodological work is joined with applications often