Übersicht			Auf mobile öffnen

Lieferstatus:	Auf Bestellung (Lieferzeit unbekannt)
Veröffentlichung:	Oktober 2025
Genre:	Schulbücher
	Advanced Statistical Models / Applied Statistics / Classification / clustering / Complex Data / data analysis / Data Analysis and Big Data / Data Mining / Data Mining and Knowledge Discovery / Data Science / Datenbanken / Large and Big Data / Learning Methods / Mathematische und statistische Software / Mixture Models / Multidimensional Data Analysis / Panel Data / Social Networks / statistical inference / statistical models / Statistical Theory and Methods / Statistics and Computing / Time Series / Wissensbasierte Systeme, Expertensysteme mehr...
ISBN:	9783031847011
EAN-Code:	9783031847011
Verlag:	Springer International Publishing
Einband:	Kartoniert
Sprache:	English
Dimensionen:	H 235 mm / B 155 mm / D
Seiten:	189
Illustration:	VIII, 189 p. 51 illus., 40 illus. in color., schwarz-weiss Illustrationen, farbige Illustrationen
Zus. Info:	EUDR exemption - product or manufacturing materials placed on the market prior to 31.12.2025.
Bewertung:	Titel bewerten / Meinung schreiben

Inhalt:

This book on statistical models and learning methods for complex data comprises a selection of peer-reviewed post-conference papers presented at the 14th Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society (CLADAG 2023), held in Salerno, Italy, September 11-13, 2023. The contributions span a variety of topics, including different approaches to clustering and classification, multidimensional data analysis, panel data, social networks, time series, statistical inference, and mixture models. These methodologies are applied to a range of empirical domains such as economics, finance, hydrology, the social sciences, education, and sports. Organized biennially by international scientific committees, the CLADAG meetings advance methodological research in multivariate statistics, with a strong focus on data analysis and classification. They facilitate the exchange of ideas in these fields and promote the dissemination of concepts, numerical methods, algorithms, and computational and applied results. Chapter "Identification of misogynistic accounts on Twitter through Graph Convolutional Networks" is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.