Hemant Ishwaran

Multivariate Statistics

Classical Foundations and ModernMachine Learning

• Gebundenes Buch

Produktbeschreibung

This book explores multivariate statistics from traditional and modern perspectives. It covers core topics like multivariate normality, MANOVA, and canonical correlation analysis, as well as modern concepts such as gradient boosting, random forests, variable importance, and causal inference.

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Produktdetails

• Verlag: Taylor & Francis Ltd
• Seitenzahl: 466
• Erscheinungstermin: 31. März 2025
• Englisch
• Abmessung: 254mm x 178mm
• ISBN-13: 9781032758794
• ISBN-10: 1032758791
• Artikelnr.: 71774170

Autorenporträt

Dr. Hemant Ishwaran's work focuses on advancing machine learning techniques for applications in public health, medicine, and informatics. His contributions include the development of open-source tools, such as R packages for his pioneering methods, including the widely-used random survival forests-a significant extension of the random forest algorithm in machine learning. His collaborations with healthcare experts have resulted in precision models for cardiovascular disease (CVD), heart transplantation, cancer staging, and resistance to gene cancer therapy.

Inhaltsangabe

Preface 1. Introduction 2. Properties of Random Vectors and Background
Material 3. Multivariate Normal Distribution 4. Linear Regression 5.
Multivariate Regression 6. Discriminant Analysis and Classification 7.
Generalization Error 8. Principal Component Analysis 9. Canonical
Correlation Analysis 10. Newton's Method 11. Steepest Descent 12. Gradient
Boosting 13. Detailed Analysis of L2Boost 14. Coordinate Descent 15. Trees
16. Random Forests 17. Random Forests Variable Selection 18. Splitting
Effect on Random Forests 19. Random Survival Forests 20. Causal Estimates
using Machine Learning