Multivariate Statistics Classical Foundations and Modern Machine Learning

Product Description

This book explores multivariate statistics from both traditional and modern perspectives. The first section covers core topics like multivariate normality MANOVA discrimination PCA and canonical correlation analysis. The second section includes modern concepts such as gradient boosting random forests variable importance and causal inference. A key theme is leveraging classical multivariate statistics to explain advanced topics and prepare for contemporary methods. For example linear models provide a foundation for understanding regu-larization with AIC and BIC leading to a deeper analysis of regularization through generalization error and the VC theorem. Discriminant analysis introduces the…