東華大學圖書館 |

Statistical learning and modeling in data analysis = methods and applications /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Statistical learning and modeling in data analysis/ edited by Simona Balzano ... [et al.].
其他題名:	methods and applications /
其他作者:	Balzano, Simona.
出版者:	Cham :Springer International Publishing : : 2021.,
面頁冊數:	viii, 182 p. :ill. (some col.), digital ;24 cm.
內容註:	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 Cramer-von Mises test of uniformity on the hypersphere (Eduardo Garcia-Portugues, 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 Jiri Dvorak) -- 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)
Contained By:	Springer Nature eBook
標題:	Statistics - Congresses. -
電子資源:	https://doi.org/10.1007/978-3-030-69944-4
ISBN:	9783030699444

Statistical learning and modeling in data analysis = methods and applications /
Statistical learning and modeling in data analysismethods and applications /[electronic resource] :edited by Simona Balzano ... [et al.]. - Cham :Springer International Publishing :2021. - viii, 182 p. :ill. (some col.), digital ;24 cm. - Studies in classification, data analysis, and knowledge organization,1431-8814. - Studies in classification, data analysis, and knowledge organization..

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 Cramer-von Mises test of uniformity on the hypersphere (Eduardo Garcia-Portugues, 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 Jiri Dvorak) -- 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)

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.

ISBN: 9783030699444

Standard No.: 10.1007/978-3-030-69944-4doiSubjects--Topical Terms:

690172
Statistics
--Congresses.

LC Class. No.: QA276.A1 / S73 2021

Dewey Class. No.: 519.5

Statistical learning and modeling in data analysis = methods and applications /
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505 0 $a 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 Cramer-von Mises test of uniformity on the hypersphere (Eduardo Garcia-Portugues, 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 Jiri Dvorak) -- 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)
520 $a 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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