Overview
- Presents state-of-the-art research at the interface of statistics and computer science
- Contributes to two highly relevant research areas: data analysis and big data
- Covers applied fields of research such as industrial engineering, econometrics, biometrics, and music data analysis
- Published in honor of Claus Weihs, Professor of Computational Statistics at TU Dortmund University
Part of the book series: Studies in Classification, Data Analysis, and Knowledge Organization (STUDIES CLASS)
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Table of contents (20 chapters)
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Front Matter
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Methodological Developments in Data Science
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Front Matter
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Perspectives on Statistics and Data Science
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Front Matter
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Statistics in Industrial Applications
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Front Matter
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Keywords
About this book
This volume presents a selection of research papers on various topics at the interface of statistics and computer science. Emphasis is put on the practical applications of statistical methods in various disciplines, using machine learning and other computational methods. The book covers fields of research including the design of experiments, computational statistics, music data analysis, statistical process control, biometrics, industrial engineering, and econometrics. Gathering innovative, high-quality and scientifically relevant contributions, the volume was published in honor of Claus Weihs, Professor of Computational Statistics at TU Dortmund University, on the occasion of his 66th birthday.
Editors and Affiliations
About the editors
Nadja Bauer is a Lecturer at Dortmund University of Applied Sciences and Arts, Germany. Her research interests include industrial statistics, music data analysis and statistical education.
Katja Ickstadt is a Professor of Mathematical Statistics with Applications in Biometrics at the Faculty of Statistics, TU Dortmund University, Germany. Her main research areas include regression modeling and methods for biomedical applications, in particular from a Bayesian viewpoint.
Karsten Lübke is a Lecturer for Statistics and Mathematics at FOM University of Applied Science, Essen, Germany. His main research interests are in statistical education and data literacy.
Gero Szepannek is a Professor of Statistics and Business Mathematics at Stralsund University of Applied Sciences, Germany. Prior to this he worked for seven years at Santander as head of scoring and rating models. His main research interests are in machine learning,computational statistics, NLP, credit risk modeling and music information retrieval.
Heike Trautmann is a Professor of Information Systems and Statistics at the University of Münster, Germany and Director of the European Research Center for Information Systems (ERCIS). Her research chiefly focuses on data science, optimization, and automated algorithm selection.
Maurizio Vichi is a Full Professor of Statistics and Chair of the Department of Statistical Sciences at Sapienza University of Rome, Italy. He also serves as Coordinating Editor of the international journal Advances in Data Analysis and Classification, published by Springer. His research interests include statistical models for clustering, classification, dimensionality reduction, and new methods for official statistics.
Bibliographic Information
Book Title: Applications in Statistical Computing
Book Subtitle: From Music Data Analysis to Industrial Quality Improvement
Editors: Nadja Bauer, Katja Ickstadt, Karsten Lübke, Gero Szepannek, Heike Trautmann, Maurizio Vichi
Series Title: Studies in Classification, Data Analysis, and Knowledge Organization
DOI: https://doi.org/10.1007/978-3-030-25147-5
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Nature Switzerland AG 2019
Softcover ISBN: 978-3-030-25146-8Published: 01 October 2019
eBook ISBN: 978-3-030-25147-5Published: 12 October 2019
Series ISSN: 1431-8814
Series E-ISSN: 2198-3321
Edition Number: 1
Number of Pages: XI, 340
Number of Illustrations: 35 b/w illustrations, 48 illustrations in colour
Topics: Statistics and Computing/Statistics Programs, Data Mining and Knowledge Discovery, Applied Statistics, Probability and Statistics in Computer Science, Mathematics in Music, Operations Research/Decision Theory