Applied Multivariate Statistical Analysis, 6th edition
Wolfgang Härdle, Universität zu Berlin;
Leopold Simar, Université Catholique de Louvain;
Matthias R. Fengler, University of St. Gallen
Springer International Publishing, 2024
ISBN: 9783031638329;
Language: English
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, Applied Multivariate Statistical Analysis 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 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.
