Statistical Learning and Modeling in Data Analysis: Methods and Applications

by Domenico Vistocco, Giovanni C. Porzio, Maurizio Vichi, Renato Salvatore, Simona Balzano

Length: 190 pages
Edition: 1
Language: English
Publisher: Springer
Publication Date: 2021-08-14
ISBN-10: 3030699439
ISBN-13: 9783030699437
Sales Rank: #0 (See Top 100 Books)

The contributions gathered in this book focus on modern methods for statistical learning and modeling in data analysis and present a series of engaging real-world applications. The book covers numerous research topics, ranging from statistical inference and modeling to clustering and factorial methods, from directional data analysis to time series analysis and small area estimation. The applications reflect new analyses in a variety of fields, including medicine, finance, engineering, marketing and cyber risk.

The book gathers selected and peer-reviewed contributions presented at the 12th Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society (CLADAG 2019), held in Cassino, Italy, on September 11–13, 2019. CLADAG promotes advanced methodological research in multivariate statistics with a special focus on data analysis and classification, and supports the exchange and dissemination of ideas, methodological concepts, numerical methods, algorithms, and computational and applied results. This book, true to CLADAG’s goals, is intended for researchers and practitioners who are interested in the latest developments and applications in the field of data analysis and classification.

Cover
Front Matter
Interpreting Effects in Generalized Linear Modeling
ACE, AVAS and Robust Data Transformations
On Predicting Principal Components Through Linear Mixed Models
Robust Model-Based Learning to Discover New Wheat Varieties and Discriminate Adulterated Kernels in X-Ray Images
A Dynamic Model for Ordinal Time Series: An Application to Consumers’ Perceptions of Inflation
Deep Learning to Jointly Analyze Images and Clinical Data for Disease Detection
Studying Affiliation Networks Through Cluster CA and Blockmodeling
Sectioning Procedure on Geostatistical Indices Series of Pavement Road Profiles
Directional Supervised Learning Through Depth Functions: An Application to ECG Waves Analysis
Penalized Versus Constrained Approaches for Clusterwise Linear Regression Modeling
Effect Measures for Group Comparisons in a Two-Component Mixture Model: A Cyber Risk Analysis
A Cramér–von Mises Test of Uniformity on the Hypersphere
On Mean And/or Variance Mixtures of Normal Distributions
Robust Depth-Based Inference in Elliptical Models
Latent Class Analysis for the Derivation of Marketing Decisions: An Empirical Study for BEV Battery Manufacturers
Small Area Estimation Diagnostics: The Case of the Fay–Herriot Model
A Comparison Between Methods to Cluster Mixed-Type Data: Gaussian Mixtures Versus Gower Distance
Exploring the Gender Gap in Erasmus Student Mobility Flows