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 mathe
matical 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.
The Basics of R.- Numerical Techniques.- Combinatorics and Discrete Distributions.- Univariate Distributions.- Univariate Statistical Analysis.- Basic Nonparametric Methods.- Multivariate Distributions.- Multivariate Statistical Analysis.- Random Numbers in R.- Advanced Graphical Techniques in R.- Symbols and Notations.
Arvustused
This is an excellent book that belongs in the libraries of most of us who use statistical computing. I love this book for a number of reasons . (David E. Booth, Technometrics, Vol. 60 (3), 2018) 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)
The Basics of R.- Numerical Techniques.- Combinatorics and Discrete Distributions.- Univariate Distributions.- Univariate Statistical Analysis.- Basic Nonparametric Methods.- Multivariate Distributions.- Multivariate Statistical Analysis.- Random Numbers in R.- Advanced Graphical Techniques in R.- Symbols and Notations.
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.
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