| Sumario: | The book *Multivariate Statistics: Classical Foundations and Modern Machine Learning* offers a comprehensive introduction to multivariate statistics, bridging classical methods such as multivariate normality, MANOVA, and principal component analysis with modern machine learning techniques including gradient boosting, random forests, and causal inference. It is structured into 20 chapters, each with exercises and bibliographies, and is designed primarily for advanced biostatistics Ph.D. students over a semester or two. The text emphasizes practical applications with case studies and discusses theoretical foundations, though it does not provide exercise solutions or direct dataset access, expecting readers to locate data independently. This work serves as a detailed resource for researchers and students seeking an integrated understanding of classical and contemporary multivariate statistical methods.
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