Statistical Learning and Modeling in Data Analysis
Simona Balzano, Giovanni C Porzio, Renato Salvatore, Domenico Vistocco, Maurizio Vichi - Editors (Springer, 2021).
This book offers a collection of papers focusing on methods for statistical learning and modeling in data analysis. A series of interesting applications are offered as well. Several research topics are covered, ranging from statistical inference and modeling to clustering and factorial methods, from directional data analysis to time series analysis and small area estimation. Applications deal with new analyses within a variety of fields of interest: medicine, finance, engineering, marketing, cyber risk, to cite a few.
The book arises as post-proceedings of the 12th meeting of the CLAssification and Data Analysis Group (CLADAG) of the Italian Statistical Society (SIS), held in Cassino (IT), on September 11–13, 2019. The first CLADAG meeting was held in 1997, in Pescara (IT). CLADAG is also a member of the International Federation of Classification Societies (IFCS), founded in 1985. CLADAG promotes advanced methodological research in multivariate statistics with a special vocation towards Data Analysis and Classification. It supports the interchange of ideas in these fields of research, including the dissemination of concepts, numerical methods, algorithms, computational and applied results. This book is thus in line with the main CLADAG goals.
Thanks to the participation of renowned speakers, coming from 28 different countries, the scientific program of the CLADAG 2019 Conference was particularly engaging. It saw 5 Keynote Lectures, 32 Invited Sessions, 16 Contributed Sessions, a Round Table, and a Data Competition.
Table of contents
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Interpreting Effects in Generalized Linear Modeling
(Alan Agresti, Claudia Tarantola, Roberta Varriale) -
ACE, AVAS and Robust Data Transformations
(Anthony C. Atkinson, Marco Riani, Aldo Corbellini, Gianluca Morelli) -
PDF On Predicting Principal Components Through Linear Mixed Models
(Simona Balzano, Maja Bozic, Laura Marcis, Renato Salvatore) -
Robust Model-Based Learning to Discover New Wheat Varieties and Discriminate Adulterated Kernels in X-Ray Images
(Andrea Cappozzo, Francesca Greselin, Thomas Brendan Murphy) -
A Dynamic Model for Ordinal Time Series: An Application to Consumers’ Perceptions of Inflation
(Marcella Corduas) -
Deep Learning to Jointly Analyze Images and Clinical Data for Disease Detection
(Federica Crobu, Agostino Di Ciaccio) -
Studying Affiliation Networks Through Cluster CA and Blockmodeling
(Daniela D’Ambrosio, Marco Serino, Giancarlo Ragozini) -
Sectioning Procedure on Geostatistical Indices Series of Pavement Road Profiles
(Mauro D’Apuzzo, Rose-Line Spacagna, Azzurra Evangelisti, Daniela Santilli, Vittorio Nicolosi) -
Directional Supervised Learning Through Depth Functions: An Application to ECG Waves Analysis
(Houyem Demni) -
Penalized Versus Constrained Approaches for Clusterwise Linear Regression Modeling
(Roberto Di Mari, Stefano Antonio Gattone, Roberto Rocci) -
Effect Measures for Group Comparisons in a Two-Component Mixture Model: A Cyber Risk Analysis
(Maria Iannario, Claudia Tarantola) -
A Cramér–von Mises Test of Uniformity on the Hypersphere
(Eduardo García-Portugués, Paula Navarro-Esteban, Juan Antonio Cuesta-Albertos) -
On Mean And/or Variance Mixtures of Normal Distributions
(Sharon X. Lee, Geoffrey J. McLachlan) -
Robust Depth-Based Inference in Elliptical Models
(Stanislav Nagy, Jiří Dvořák) -
Latent Class Analysis for the Derivation of Marketing Decisions: An Empirical Study for BEV Battery Manufacturers
(Friederike Paetz) -
Small Area Estimation Diagnostics: The Case of the Fay–Herriot Model
(Maria Chiara Pagliarella) -
A Comparison Between Methods to Cluster Mixed-Type Data: Gaussian Mixtures Versus Gower Distance
(Monia Ranalli, Roberto Rocci) -
Exploring the Gender Gap in Erasmus Student Mobility Flows
(Marialuisa Restaino, Ilaria Primerano, Maria Prosperina Vitale)