Incluye referencias bibliograficas p. [667]-707 e índices

Introduction and preview -- data and databases -- Random vectors and matrices -- Nonparametric density estimation -- Model assessment and selection in multiple regression -- Multivariate regression -- Linear dimensionality reduction -- Linear discriminant analysis -- Recursive partitioning and tree-based methods -- Artificial neural networks -- Support vector machines -- Cluster analysis -- Multidimensional scaling and distance geometry -- Committee machines -- Latent variable models for blind source separation -- Nonlinear dimensionality reduction and manifold learning -- Correspondence analysis

"These exciting developments, which led to the introduction of many innovative statistical tools for high-dimensional data analysis, are described here in detail. The author takes a broad perspective; for the first time in a book on multivariate analysis, nonlinear methods are discussed in detail as well as linear methods. Techniques covered range from traditional multivariate methods, such as multiple regression, principal components, canonical variates, linear discriminant analysis, factor analysis, clustering, multidimensional scaling, and correspondence analysis, to the newer methods of density estimation, projection pursuit, neural networks, multivariate reduced-rank regression, nonlinear manifold learning, bagging, boosting, random forests, independent component analysis, support vector machines, and classification and regression trees. Another unique feature of this book is the discussion of database management systems."--P. web editorial

Compra 090319