Morlini I., Minerva T., Vichi M. (eds.) Advances in Statistical Models for Data Analysis

New York: Springer, 2015. - 268 p. 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 Designs
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
Nonmetric 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 Multistate Models to Analyze Students’ University Pathways.