6th. ed. - Springer, 2024. - 610 p. - ISBN 3031638328.
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.
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