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Statistical Learning and Modeling in Data Analysis: Methods and Applications de Simona Balzano

Statistical Learning and Modeling in Data Analysis: Methods and Applications: Studies in Classification, Data Analysis, and Knowledge Organization

Editat de Simona Balzano, Giovanni C. Porzio, Renato Salvatore, Domenico Vistocco, Maurizio Vichi
en Limba Engleză Paperback – 14 iul 2021
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.

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Specificații

ISBN-13: 9783030699437
ISBN-10: 3030699439
Ilustrații: VIII, 182 p. 40 illus., 26 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.28 kg
Ediția:1st ed. 2021
Editura: Springer International Publishing
Colecția Springer
Seria Studies in Classification, Data Analysis, and Knowledge Organization

Locul publicării:Cham, Switzerland

Cuprins

Chapter 1 - Interpreting Effects in Generalized Linear Modeling (Alan Agresti, Claudia Tarantola, and Roberta Varriale).- Chapter 2 - ACE, AVAS and Robust Data Transformations: Performance of Investment Funds (Anthony C. Atkinson, Marco Riani, Aldo Corbellini, and Gianluca Morelli).- Chapter 3 - Predictive Principal Component Analysis (Simona Balzano, Maja Bozic, Laura Marcis, and Renato Salvatore).- Chapter 4 - Robust model-based learning to discover new wheat varieties and discriminate adulterated kernels in X-ray images (Andrea Cappozzo, Francesca Greselin, and Thomas Brendan Murphy).- Chapter 5 - A dynamic model for ordinal time series: an application to consumers’ perceptions of inflation (Marcella Corduas).- Chapter 6 - Deep learning to jointly analyze images and clinical data for disease detection (Federica Crobu and Agostino Di Ciaccio).- Chapter 7 -Studying Affiliation Networks through Cluster CA and Blockmodeling (Daniela D’Ambrosio, Marco Serino, and Giancarlo Ragozini).- Chapter 8 - Sectioning Procedure on Geostatistical Indices Series of Pavement Road Profiles (Mauro D’Apuzzo, Rose-Line Spacagna, Azzurra Evangelisti, Daniela Santilli, and Vittorio Nicolosi).-  Chapter 9 - Directional supervised learning through depth functions: an application to ECG waves analysis (Houyem Demni).- Chapter 10 - Penalized vs. contrained approaches for clusterwise linear regression modelling (Roberto Di Mari, Stefano Antonio Gattone, and Roberto Rocci).- Chapter 11 - Effect measures for group comparisons in a two-component mixture model: a cyber risk analysis (Maria Iannario and Claudia Tarantola).- Chapter 12 - A Cramér–von Mises test of uniformity on the hypersphere (Eduardo García-Portugués, Paula Navarro-Esteban, and Juan Antonio Cuesta-Albertos).- Chapter 13 - On mean and/or variance mixtures of normal distributions (Sharon X. Lee and Geoffrey J. McLachlan).- Chapter 14 - Robust depth-based inference in elliptical models (Stanislav Nagy and Jiří Dvořák).- Chapter 15 - Latent class analysis for the derivation of marketing decisions: An empirical study for BEV battery manufacturers (Friederike Paetz).- Chapter 16 - Small Area Estimation Diagnostics: the Case of the Fay-Herriot Model (Maria Chiara Pagliarella).- Chapter 17 - A comparison between methods to cluster mixed-type data: Gaussian mixtures versus Gower distance (Monia Ranalli and Roberto Rocci).- Chapter 18 - Exploring the gender gap in Erasmus student mobility flows (Marialuisa Restaino, Ilaria Primerano, and Maria Prosperina Vitale).

Notă biografică

Simona Balzano is an Assistant Professor of Statistics at the University of Cassino and Southern Lazio, Italy, where she teaches on data analysis and research methods in management. Her recent research activities concern multivariate analysis, partial least squares regression and path-modeling, and structural equation modeling. Her interests include applications in performance analysis, consumer analysis, and other related fields of business and industry.
Giovanni C. Porzio is a Professor of Statistics at the University of Cassino and Southern Lazio, Italy, where he has previously served as Department Head and Director of Graduate Studies in Economics. His research interests include directional statistics, statistical learning, nonparametric multivariate analysis and data depth, graphical methods and data visualization. His research work has appeared in several journals and books.

Renato Salvatore is an Assistant Professor of Economic Statistics at the University of Cassino and Southern Lazio, Italy. He has co-authored papers, book chapters, and proceedings on sampling surveys, small area estimation, and multivariate analysis. In addition, he has been co-editor of several conference proceedings, and currently teaches on economic statistics and market analysis.
Domenico Vistocco is an Associate Professor of Statistics at the University of Naples Federico II, Italy. He is an Associate Editor of Computational Statistics and Editorial Manager of Statistica Applicata - Italian Journal of Applied Statistics. He has co-authored two books on quantile regression and ca. 100 papers, book chapters, proceedings, post-proceedings and editorials on various statistical topics. He teaches on statistical inference, data analysis, applied statistics and statistical programming. His research interests include quantile regression, computational statistics, statistical models, exploratory data analysis and visualization.

Maurizio Vichi is a Professor of Statistics and Chair of the Department of Statistical Sciences at Sapienza University of Rome, Italy. He is also Coordinating Editor of the international journal Advances in Data Analysis and Classification and Acting Chair of the European Statistical Advisory Committee of the EU. He teaches on multivariate statistics and data analysis and statistical modeling. His research interests include statistical models for clustering, classification, dimensionality reduction, composite indicators, PLS, SEM and new methods for official statistics based on smart statistics and big data analysis. He is the author of more than 150 papers, mainly published in peer-reviewed international statistics journals.



Textul de pe ultima copertă

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.

Caracteristici

Focuses on modern methods for statistical learning and modeling in data analysis Presents real-world applications in medicine, finance, engineering, marketing and cyber risk Will appeal to researchers and practitioners alike