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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.
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.
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Take 20% off your first order
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