Statistical learning and modeling in data analysis: methods and applications
by Balzano, Simona Porzio, Giovanni C. Salvatore, Renato Vichi, Maurizio Vistocco, Domenico
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…
Read More ↓Title Statements
- Full title :Statistical learning and modeling in data analysis : methods and applications / Simona Balzano, Giovanni C. Porzio, Renato Salvatore, Domenico Vistocco, Maurizio Vichi, editors.
Summary
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.
Read More ↓Edition Statements
- Publication Date:©2021
Publication Statements
- Publisher(s):Springer
- Place of Publication:Cham
- Language:eng
- Publication Date:©2021
The Physical Object
- Description:1 online resource : : illustrations (some color)
Edition Identifiers
- ISBN:9783030699444, 3030699447
- DOI:10.1007/978-3-030-69944-4
- oclc:on1260401139
- LC Classification:QA276.A1
- Dewey Decimal:519.5
Table of Contents
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) -…
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