Authors:
- Covers basic mathematical, statistical and programming problems in computational statistics
- Addresses both univariate and multivariate statistical data analysis and applications in finance, the life sciences and other disciplines
- Features R sniplets in the text and computer programs on GitHub that allow all examples to be fully reproduced
- Provides a smooth introduction to R for statistical computing
- Includes supplementary material: sn.pub/extras
Part of the book series: Statistics and Computing (SCO)
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Table of contents (10 chapters)
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Front Matter
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Back Matter
About this book
This textbook on computational statistics presents tools and concepts of univariate and multivariate statistical data analysis with a strong focus on applications and implementations in the statistical software R. It covers mathematical, statistical as well as programming problems in computational statistics and contains a wide variety of practical examples. In addition to the numerous R sniplets presented in the text, all computer programs (quantlets) and data sets to the book are available on GitHub and referred to in the book. This enables the reader to fully reproduce as well as modify and adjust all examples to their needs.
The book is intended for advanced undergraduate and first-year graduate students as well as for data analysts new to the job who would like a tour of the various statistical tools in a data analysis workshop. The experienced reader with a good knowledge of statistics and programming might skip some sections on univariate models and enjoy the various ma
thematical roots of multivariate techniques.The Quantlet platform quantlet.de, quantlet.com, quantlet.org is an integrated QuantNet environment consisting of different types of statistics-related documents and program codes. Its goal is to promote reproducibility and offer a platform for sharing validated knowledge native to the social web. QuantNet and the corresponding Data-Driven Documents-based visualization allows readers to reproduce the tables, pictures and calculations inside this Springer book.
Keywords
- 62-XX, 62G07, 62G08, 62H15, 62Jxx
- computational statistics
- multivariate statistical analysis
- Quantlet
- high-dimensional data analysis
- R sniplet
- regression
- visualization
- data reproducibility
- univariate statistical analysis
- programming language R
- nonparametric methods
- random numbers
- numerical techniques in statistics
- graphical techniques
Reviews
“The book deals with different tools and concepts regarding statistical analysis. … The book is intended for advanced undergraduate and even MSc students, as well as PhD student, working with different statistical techniques.” (Florin Gorunescu, zbMATH 1392.62001, 2018)
Authors and Affiliations
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CASE – Center for Applied Statistics and, Humboldt-Universität zu Berlin CASE – Center for Applied Statistics and, Berlin, Germany
Wolfgang Karl Härdle
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Econometrics and Statistics, Technische Universität Dresden Econometrics and Statistics, Dresden, Germany
Ostap Okhrin
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Faculty of Business and Economics, Universität Augsburg Faculty of Business and Economics, Augsburg, Germany
Yarema Okhrin
About the authors
Wolfgang Karl Härdle is the Ladislaus von Bortkiewicz Professor of Statistics at the Humboldt-Universität zu Berlin and Director of C.A.S.E. (Center for Applied Statistics and Economics), Director of the CRC-649 (Collaborative Research Center) “Economic Risk” as well as Director of the IRTG 1792 “High Dimensional Non-stationary Time Series”. He teaches quantitative finance and semi-parametric statistics. His research focuses on dynamic factor models, multivariate statistics, tail event curves in finance and computational statistics. He is an elected member of the ISI (International Statistical Institute) and foreign expert professor at Xiamen University, China, and a senior fellow of Sim Kee Boon Institute of Financial Economics at the Singapore Management University.
Ostap Okhrin is Professor of Econometrics and Statistics, especially in Transportation at the Dresden University of Technology. He worked at the European University Viadrin
a and later was an Assistant and then Associate Professor for Statistics of Financial Markets at the Humboldt University of Berlin and one of the principal investigators of the CRC-649 (Collaborative Research Center) „Economic Risk". He teaches multivariate and mathematical statistics. His research focuses on multivariate models in particular copulas and financial econometrics.
Yarema Okhrin is Professor of Statistics at the University of Augsburg. He teaches financial econometrics and multivariate data analysis. His research focuses on multivariate statistics and econometrics with applications to finance, statistical surveillance and computational statistics. He previously worked as Assistant Professor of Econometrics at the University of Bern and at the European University Viadrina.
Bibliographic Information
Book Title: Basic Elements of Computational Statistics
Authors: Wolfgang Karl Härdle, Ostap Okhrin, Yarema Okhrin
Series Title: Statistics and Computing
DOI: https://doi.org/10.1007/978-3-319-55336-8
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Nature Switzerland AG 2017
Hardcover ISBN: 978-3-319-55335-1Published: 10 October 2017
Softcover ISBN: 978-3-319-85631-5Published: 14 August 2018
eBook ISBN: 978-3-319-55336-8Published: 29 September 2017
Series ISSN: 1431-8784
Series E-ISSN: 2197-1706
Edition Number: 1
Number of Pages: XXI, 305
Number of Illustrations: 31 b/w illustrations, 66 illustrations in colour
Topics: Statistics and Computing/Statistics Programs, Statistical Theory and Methods, Probability and Statistics in Computer Science, Biostatistics, Statistics for Business, Management, Economics, Finance, Insurance, Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences