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Applications in Statistical Computing

From Music Data Analysis to Industrial Quality Improvement

  • Nadja Bauer
  • Katja Ickstadt
  • Karsten Lübke
  • Gero Szepannek
  • Heike Trautmann
  • Maurizio Vichi
Book

Table of contents

  1. Front Matter
    Pages i-xi
  2. Methodological Developments in Data Science

    1. Front Matter
      Pages 1-1
    2. Claudia Wigmann, Laura Lange, Wolfgang Vautz, Katja Ickstadt
      Pages 31-48
    3. Houyem Demni, Amor Messaoud, Giovanni C. Porzio
      Pages 49-60
  3. Computational Statistics

  4. Perspectives on Statistics and Data Science

    1. Front Matter
      Pages 125-125
    2. Katharina Morik
      Pages 127-138
    3. Karsten Lübke, Matthias Gehrke, Norman Markgraf
      Pages 139-150
    4. Ursula Garzcarek, Detlef Steuer
      Pages 151-169
  5. Statistics in Econometric Applications

    1. Front Matter
      Pages 171-171
    2. Walter Krämer, Peter N. Posch
      Pages 187-199
  6. Statistics in Industrial Applications

    1. Front Matter
      Pages 217-217
    2. Nadja Malevich, Christine H. Müller, Michael Kansteiner, Dirk Biermann, Manuel Ferreira, Wolfgang Tillmann
      Pages 233-249
    3. Sermad Abbas, Roland Fried, Jens Heinrich, Melanie Horn, Mirko Jakubzik, Johanna Kohlenbach et al.
      Pages 251-269
    4. Sebastian Hoffmeister, Andrea Geistanger
      Pages 271-287
    5. Jochen Deuse, Mario Wiegand, Kirsten Weisner
      Pages 289-301
  7. Statistics in Music Applications

    1. Front Matter
      Pages 303-303
    2. Martin Ebeling, Günther Rötter
      Pages 327-340

About this book

Introduction

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.

Keywords

Statistical computing Data science Machine learning Computational statistics Classification Clustering Data analysis Big data Statistical process control Music data analysis Industrial engineering Industrial quality improvement Biometrics Econometrics Design of experiments

Editors and affiliations

  • Nadja Bauer
    • 1
  • Katja Ickstadt
    • 2
  • Karsten Lübke
    • 3
  • Gero Szepannek
    • 4
  • Heike Trautmann
    • 5
  • Maurizio Vichi
    • 6
  1. 1.Department of Computer ScienceDortmund University of Applied Sciences and ArtsDortmundGermany
  2. 2.Faculty of StatisticsTU Dortmund UniversityDortmundGermany
  3. 3.Institute for Empirical Research and StatisticsFOM University of Applied SciencesEssenGermany
  4. 4.School of Business StudiesHOST University of Applied Sciences StralsundStralsundGermany
  5. 5.Department of Information SystemsUniversity of MünsterMünsterGermany
  6. 6.Department of Statistical SciencesSapienza University of RomeRomeItaly

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-030-25147-5
  • Copyright Information Springer Nature Switzerland AG 2019
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-030-25146-8
  • Online ISBN 978-3-030-25147-5
  • Series Print ISSN 1431-8814
  • Series Online ISSN 2198-3321
  • Buy this book on publisher's site