Advances in Statistical Models for Data Analysis This edited volume focuses on recent research results in classification multivariate statistics and machine learning and highlights advances in statistical models for data analysis. The volume provides both methodological developments and contributions to a wide range of application areas such as economics marketing education social sciences and environment. The papers in this volume were first presented at the 9th biannual meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society held in September 2013 at the University of Modena and Reggio Emilia Italy Using the dglars Package to Estimate a Sparse Generalized Linear Model A Depth function for Geostatistical Functional Data Robust Clustering of EU Banking Data Sovereign Risk and Contagion Effects in the Eurozone: a Bayesian Stochastic Correlation Model Female Labour Force Participation and Selection Effect: Southern vs Eastern European Countries Asymptotics in Survey Sampling for High Entropy Sampling Design A Note On the Use of Recursive Partitioning in Causal Inference Meta Analysis of Poll Accuracy Measures: A Multilevel Approach Families of Parsimonious Finite Mixtures of Regression Models Quantile Regression for Clustering and Modeling Data Non metric MDS Consensus Community Detection The performance of the Gradient like Influence Measure in Generalized Linear Mixed Models New Flexible Probability Distributions for Ranking Data Robust Estimation of Regime Switching Models Incremental Visualization of Categorical Data A new Proposal for Tree Model Selection and Visualization Object Oriented Bayesian Network to Deal with Measurement Error in Household Surveys Comparing Fuzzy and Multidimensional Methods to Evaluate Well being in European Regions Cluster Analysis of Three way Atmospheric Data Asymmetric CLUster Analysis Based on SKEW symmetry: ACLUSKEW Parsimonious Generalized Linear Gaussian Cluster Weighted Models New perspectives for the MDC Index in Social Research Fields Clustering Methods for Ordinal Data: A Comparison Between Standard and New Approaches Novelty Detection with One class Support Vector Machines Using Discrete time Multi State Models to Analyze Students University Pathways