Statistical Models and Methods for Data Science (eBook)

eBook Download: PDF
2023 | 1st ed. 2023
VIII, 188 Seiten
Springer International Publishing (Verlag)
978-3-031-30164-3 (ISBN)

Lese- und Medienproben

Statistical Models and Methods for Data Science -
Systemvoraussetzungen
128,39 inkl. MwSt
  • Download sofort lieferbar
  • Zahlungsarten anzeigen

This book focuses on methods and models in classification and data analysis and presents real-world applications at the interface with data science. Numerous topics are covered, ranging from statistical inference and modelling to clustering and factorial methods, and from directional data analysis to time series analysis and small area estimation. The applications deal with new developments in a variety of fields, including medicine, finance, engineering, marketing, and cyber risk.

The contents comprise selected and peer-reviewed contributions presented at the 13th Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society, CLADAG 2021, held (online) in Florence, Italy, on September 9-11, 2021. 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 at the interface between classification and data science.



Leonardo Grilli is a Full Professor of Statistics at the University of Florence, Italy and the Director of the Master's program in Statistics and Data Science and a member of the board of the Doctoral Program in Development Economics and Local Systems. His teaching activity focuses on introductory statistics and statistical modelling, including generalized linear models and multilevel models. His research mainly concerns random effects models for multilevel analysis, with methodological advances about the specification and estimation of models in complex frameworks such as duration data, multivariate qualitative responses, informative sampling designs, and sample selection bias. He has also made contributions to causal inference in the potential outcomes framework, IRT models, latent growth curve models, mixture models, and quantile regression.

Monia Lupparelli is an Associate Professor of Statistics at the University of Florence, Italy, and a member of the board of the Doctoral Program in Statistics at the University of Bologna. She teaches the foundations of statistics and of statistical modelling in bachelor's and master's degree programs. She also gives specialised courses on graphical models and categorical data analysis for doctoral and post-doctoral students.  Her research activity is mainly grounded in network modelling, latent variable models, and marginal models with applications to the analysis of longitudinal and categorical data in social and biomedical sciences.

Carla Rampichini is a Full Professor of Statistics and Head of the Department of Statistics, Computer science, and Applications at the University of Florence, Italy. Her teaching activity focuses on introductory statistics, multivariate analysis and statistical modelling. Her research interests relate to random effects models for multilevel analysis, program evaluation, and causal inference. Her methodological work is joined with applications often concerning the effectiveness of universities. She is a member of the Royal Statistical Society and the Italian Statistical Society. She is the Editor-in-Chief of Statistical Methods and Applications.

Emilia Rocco is an Associate Professor of Statistics at the University of Florence, Italy. Her teaching activity focuses on introductory statistics and survey sampling theory and applications. Her research interests relate to informative and not-informative sample designs useful for studying rare and clustered populations, capture-recapture methodologies, methods of treatment of nonresponse, indicators of representativeness for monitoring the risk of selection bias when non-response or a convenience sample occurs, small area estimation methods, geoadditive models and their applications to the analysis of environmental and socio-economic phenomena. She is a member of the International Association of Survey Statisticians and of the Italian Statistical Society.

Maurizio Vichi is a Full Professor of Statistics at Sapienza University of Rome, Italy, and Head of the Department of Statistical Sciences. He is the founding President and current Vice-President of the Federation of European National Statistical Societies, and a former President of the Italian Statistical Society and the International Federation of Classification Societies. 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 and is the author of more than 150 papers, mainly on new multivariate statistical methods for data analysis, including Big Data and statistical tools for decision making.

 

Erscheint lt. Verlag 25.8.2023
Reihe/Serie Studies in Classification, Data Analysis, and Knowledge Organization
Studies in Classification, Data Analysis, and Knowledge Organization
Zusatzinfo VIII, 188 p. 32 illus., 19 illus. in color.
Sprache englisch
Themenwelt Informatik Theorie / Studium Künstliche Intelligenz / Robotik
Mathematik / Informatik Mathematik Statistik
Mathematik / Informatik Mathematik Wahrscheinlichkeit / Kombinatorik
Schlagworte classification • Clustering • Data Analysis • Data Science • Data Science applications • machine learning • multivariate analysis • Statistical Inference • Statistical Learning • Statistical Models • statistical software
ISBN-10 3-031-30164-1 / 3031301641
ISBN-13 978-3-031-30164-3 / 9783031301643
Haben Sie eine Frage zum Produkt?
PDFPDF (Wasserzeichen)
Größe: 2,9 MB

DRM: Digitales Wasserzeichen
Dieses eBook enthält ein digitales Wasser­zeichen und ist damit für Sie persona­lisiert. Bei einer missbräuch­lichen Weiter­gabe des eBooks an Dritte ist eine Rück­ver­folgung an die Quelle möglich.

Dateiformat: PDF (Portable Document Format)
Mit einem festen Seiten­layout eignet sich die PDF besonders für Fach­bücher mit Spalten, Tabellen und Abbild­ungen. Eine PDF kann auf fast allen Geräten ange­zeigt werden, ist aber für kleine Displays (Smart­phone, eReader) nur einge­schränkt geeignet.

Systemvoraussetzungen:
PC/Mac: Mit einem PC oder Mac können Sie dieses eBook lesen. Sie benötigen dafür einen PDF-Viewer - z.B. den Adobe Reader oder Adobe Digital Editions.
eReader: Dieses eBook kann mit (fast) allen eBook-Readern gelesen werden. Mit dem amazon-Kindle ist es aber nicht kompatibel.
Smartphone/Tablet: Egal ob Apple oder Android, dieses eBook können Sie lesen. Sie benötigen dafür einen PDF-Viewer - z.B. die kostenlose Adobe Digital Editions-App.

Buying eBooks from abroad
For tax law reasons we can sell eBooks just within Germany and Switzerland. Regrettably we cannot fulfill eBook-orders from other countries.

Mehr entdecken
aus dem Bereich
der Praxis-Guide für Künstliche Intelligenz in Unternehmen - Chancen …

von Thomas R. Köhler; Julia Finkeissen

eBook Download (2024)
Campus Verlag
38,99
Wie du KI richtig nutzt - schreiben, recherchieren, Bilder erstellen, …

von Rainer Hattenhauer

eBook Download (2023)
Rheinwerk Computing (Verlag)
24,90