000			03255nam a2200229Ia 4500
003			NULRC
005			20250520102805.0
008			250520s9999 xx 000 0 und d
020			_a9780387781884
040			_cNULRC
050			_aQA 278 .I94 2013
100			_aIzenman, Alan Julian _eauthor
245		0	_aModern multivariate statistical techniques : _bregression, classification, and manifold learning / _cAlan Julian Izenman
260			_aNew York : _bSpringer, _cc2013
300			_axxv, 733 pages : _bcolor illustrations ; _c24 cm.
365			_bUSD59.33
504			_aIncludes bibliographical references and index.
505			_a1. Introduction and preview -- 2. Data and databases -- 3. Random vectors and matrices -- 4. Nonparametric density estimation -- 5. Model assessment and selection in multiple regression -- 6. Multivariate regression -- 7. Linear dimensionality reduction -- 8. Linear discriminant analysis -- 9. Recursive partitioning and tree-based methods -- 10. Artificial neural networks -- 11. Support vector machines -- 12. Cluster analysis -- 13. Multidimensional scaling and distance geometry -- 14. Committee machines -- 15. Latent variable models for blind source separation -- 16. Nonlinear dimensionality reduction and manifold learning -- 17. Correspondence analysis.
520			_aRemarkable advances in computation and data storage and the ready availability of huge data sets have been the keys to the growth of the new disciplines of data mining and machine learning, while the enormous success of the Human Genome Project has opened up the field of bioinformatics. 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. This book is appropriate for advanced undergraduate students, graduate students, and researchers in statistics, computer science, artificial intelligence, psychology, cognitive sciences, business, medicine, bioinformatics, and engineering. Familiarity with multivariable calculus, linear algebra, and probability and statistics is required. The book presents a carefully-integrated mixture of theory and applications, and of classical and modern multivariate statistical techniques, including Bayesian methods. There are over 60 interesting data sets used as examples in the book, over 200 exercises, and many color illustrations and photographs.
650			_aMULTIVARIATE ANALYSIS
942			_2lcc _cBK
999			_c15395 _d15395