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ReviewFrom the reviews:
"This book will be enjoyed by those who wish to understand the current state of multivariate statistical analysis in an age of high-speed computation and large data sets. … persons interested in learning new trends of multivariate methods would find Izenman’s book very helpful. … The full-color graphics is quite impressive - well done! There are numerous real-data examples from many scientific disciplines so that not only statisticians may find this book useful and interesting." (Simo Puntanen, International Statistical Review, Vol. 76 (3), 2008)
"The book describes how to manage data for maintaining and querying large databases. … I recommend this book for advanced students in statistics and related profiles as, computer science, artificial intelligence, cognitive sciences, bio-informatics, and the involved different branches of engineering. More than 60 data sets are used for working out as examples. More than 200 exercises are presented in the book." (J. A. Rouen, Revista Investigación Operacional, Vol. 30 (2), 2009)
"For the first time in a book on multivariate analysis, nonlinear as well as linear methods are discussed in detail. … Another unique feature of this book is the discussion of database management systems. This book is appropiate for advanced undergraduate students, graduate students, and researchers in statistics, computer science, artificial intelligence, psychology, cognitive sciences, business, medicine, bioinformatics and engineering. … The book presents a carefully-integrated mixture of theory and applications, and of classical and modern multivariate statistical techniques, including Bayesian methods." (T. Postelnicu, Zentralblatt MATH, Vol. 1155, 2009)
Product DescriptionRemarkable 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.
Product Details
- Hardcover: 734 pages
- Publisher: Springer; 1 edition (August 28, 2008)
- Language: English
- ISBN-10: 0387781889
- ISBN-13: 978-0387781884
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具体目录，可以上Amazon找，需要的下。书中代码由r写成，也有matlab代码，因此学习matlab的同学也可以下载看看。
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