Overview
- Provides a comprehensive treatment of multivariate statistical analysis, including approaches to high-dimensional data
- Presents modern machine learning methods for dimension reduction and data visualization
- Features numerous examples, exercises and supplementary computer code, equipping readers to reproduce all computations
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About this book
Now in its sixth edition, this textbook presents the tools and concepts used in multivariate data analysis in a style accessible for non-mathematicians and practitioners. Each chapter features hands-on exercises that showcase applications across various fields of multivariate data analysis. These exercises utilize high-dimensional to ultra-high-dimensional data, reflecting real-world challenges in big data analysis.
For this new edition, the book has been updated and revised and now includes new chapters on modern machine learning techniques for dimension reduction and data visualization, namely locally linear embedding, t-distributed stochastic neighborhood embedding, and uniform manifold approximation and projection, which overcome the shortcomings of traditional visualization and dimension reduction techniques.
Solutions to the book’s exercises are supplemented by R and MATLAB or SAS computer code and are available online on the Quantlet and Quantinar platforms. Practical exercises from this book and their solutions can also be found in the accompanying Springer book by W.K. Härdle and Z. Hlávka: Multivariate Statistics - Exercises and Solutions.
Keywords
- Multivariate Data Analysis
- Multivariate Statistics
- Multivariate Analysis
- Dimension Reduction
- Machine Learning Techniques
- Variable Selection
- Multivariate Classification
- Cluster Analysis
- Discriminant Analysis
- Conjoint Measurement Analysis
- Data Visualization
- Hypothesis Testing
- Big Data Analysis
- Computationally Intensive Techniques
- Lasso and Elastic Net
- Projection Pursuit
- Applications in Finance
- Quantitative Finance
Table of contents (23 chapters)
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Front Matter
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Descriptive Techniques
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Front Matter
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Multivariate Random Variables
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Front Matter
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Authors and Affiliations
About the authors
Wolfgang Karl Härdle is the Ladislaus von Bortkiewicz Professor of Statistics at the Humboldt-Universität zu Berlin, Germany. He is also a Professor at the Faculty of Mathematics and Physics at the Charles University in Prague, Czech Republic. He teaches quantitative finance and semi-parametric statistics. His research focuses on modern machine learning, multivariate statistics in finance and computational statistics. He is an elected member of the ISI (International Statistical Institute) and the director of the Institute of Digital Assets, Academy of Economic Sciences, Bucharest, Romania.
Léopold Simar is an Emeritus Professor of Statistics at UCLouvain, Louvain-la-Neuve, Belgium. He has taught mathematical statistics, multivariate analysis, bootstrap methods in statistics and econometrics at several European universities. His research focuses on non-parametric and semi-parametric methods and bootstrap techniques in statistics and econometrics. He is an elected member of the ISI and the past president of the Belgian Statistical Society and is a regular Visiting Professor at the Sapienza University of Rome, Italy and at the Toulouse School of Economics, France.
Matthias R. Fengler is a Professor of Econometrics at the School of Economics and Political Science at the University of St. Gallen, Switzerland. His area of specialization is Financial Econometrics and he works in asset pricing, volatility modeling, risk-management, and the analysis of financial text data. In collaboration with colleagues from both academia and industry, he initiated the University of St. Gallen's Impact Award-winning project Monitoring Consumption Switzerland, which analyzes and explores private debit and credit card expenditures and payment behavior in Switzerland.
Bibliographic Information
Book Title: Applied Multivariate Statistical Analysis
Authors: Wolfgang Karl Härdle, Léopold Simar, Matthias R. Fengler
DOI: https://doi.org/10.1007/978-3-031-63833-6
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024
Softcover ISBN: 978-3-031-63832-9Published: 29 September 2024
eBook ISBN: 978-3-031-63833-6Published: 28 September 2024
Edition Number: 6
Number of Pages: XV, 613
Number of Illustrations: 62 b/w illustrations, 141 illustrations in colour
Topics: Statistical Theory and Methods, Statistics for Business, Management, Economics, Finance, Insurance, Applications of Mathematics, Economic Theory/Quantitative Economics/Mathematical Methods, Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences, Applied Statistics