Remarkable 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 ...
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Remarkable 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.
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Add this copy of Modern Multivariate Statistical Techniques: Regression, to cart. $26.54, good condition, Sold by Treasure Island rated 5.0 out of 5 stars, ships from Waltham, MA, UNITED STATES, published 2008 by Springer.
Add this copy of Modern Multivariate Statistical Techniques: Regression, to cart. $27.47, good condition, Sold by BooksRun rated 4.0 out of 5 stars, ships from Philadelphia, PA, UNITED STATES, published 2008 by Springer.
Add this copy of Modern Multivariate Statistical Techniques: Regression, to cart. $56.25, very good condition, Sold by Jero Books and Templet Co. rated 5.0 out of 5 stars, ships from Santa Monica, CA, UNITED STATES, published 2008 by Springer.
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Seller's Description:
Very Good. No DJ Issued. Computer Vision & Pattern Reco. 8vo. 2008 Edition (2008. ) Hardcover without dust jacket as issued. 8vo with 731 pages. The book is in very good condition slight bump to one corner. Interior clean and tight, No markings. No online access or CD-ROM or digital access codes if applicable! "a completely new and refreshing approach to statistics and data exploration. comprehensive volume on multivariate statistical analysis. Highly recommended for both Statistics and Computer Science/Electrical Engineering majors." Blue spine/White-Green text.
Add this copy of Modern Multivariate Statistical Techniques: Regression, to cart. $64.40, good condition, Sold by Bonita rated 4.0 out of 5 stars, ships from Santa Clarita, CA, UNITED STATES, published 2008 by Springer.
