Applied Multivariate Statistical Analysis

Wolfgang Karl Härdle

ISBN 10: 3031638328 / ISBN 13: 9783031638329

Verlag: Springer International Publishing, Springer International Publishing Sep 2024, 2024

Beschreibung

Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 632 pp. Englisch.

Bestandsnummer des Verkäufers 9783031638329

Bibliografische Details

• Titel: Applied Multivariate Statistical Analysis
• Verlag: Springer International Publishing, Springer International Publishing Sep 2024
• Erscheinungsjahr: 2024
• Sprache: Englisch
• ISBN-10: 3031638328
• ISBN-13: 9783031638329
• Einband: Taschenbuch
• Zustand: Neu

Über diesen Titel

Inhaltsangabe

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.

Über die Autorin bzw. den Autor

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.