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Modern Multivariate Statistical Techniques
Regression, Classification, and Manifold Learning
Buch von Alan J. Izenman
Sprache: Englisch

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Beschreibung
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.
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.
Zusammenfassung

Describes database management systems for maintaining and querying large databases

Provides detailed descriptions of linear and nonlinear data-mining and machine-learning techniques

Integrates theory, real-data examples from many scientific disciplines, exercises, and full-color graphics for explaining the various classical and new multivariate statistical techniques

Includes supplementary material: [...]

Inhaltsverzeichnis
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.
Details
Erscheinungsjahr: 2008
Fachbereich: Wahrscheinlichkeitstheorie
Genre: Importe, Mathematik
Rubrik: Naturwissenschaften & Technik
Medium: Buch
Reihe: Springer Texts in Statistics
Inhalt: xxv
733 S.
ISBN-13: 9780387781884
ISBN-10: 0387781889
Sprache: Englisch
Herstellernummer: 10945445
Ausstattung / Beilage: HC runder Rücken kaschiert
Einband: Gebunden
Autor: Izenman, Alan J.
Hersteller: Springer US
Springer New York
Springer US, New York, N.Y.
Springer Texts in Statistics
Verantwortliche Person für die EU: Books on Demand GmbH, In de Tarpen 42, D-22848 Norderstedt, info@bod.de
Maße: 241 x 160 x 44 mm
Von/Mit: Alan J. Izenman
Erscheinungsdatum: 28.08.2008
Gewicht: 1,429 kg
Artikel-ID: 101831098
Zusammenfassung

Describes database management systems for maintaining and querying large databases

Provides detailed descriptions of linear and nonlinear data-mining and machine-learning techniques

Integrates theory, real-data examples from many scientific disciplines, exercises, and full-color graphics for explaining the various classical and new multivariate statistical techniques

Includes supplementary material: [...]

Inhaltsverzeichnis
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.
Details
Erscheinungsjahr: 2008
Fachbereich: Wahrscheinlichkeitstheorie
Genre: Importe, Mathematik
Rubrik: Naturwissenschaften & Technik
Medium: Buch
Reihe: Springer Texts in Statistics
Inhalt: xxv
733 S.
ISBN-13: 9780387781884
ISBN-10: 0387781889
Sprache: Englisch
Herstellernummer: 10945445
Ausstattung / Beilage: HC runder Rücken kaschiert
Einband: Gebunden
Autor: Izenman, Alan J.
Hersteller: Springer US
Springer New York
Springer US, New York, N.Y.
Springer Texts in Statistics
Verantwortliche Person für die EU: Books on Demand GmbH, In de Tarpen 42, D-22848 Norderstedt, info@bod.de
Maße: 241 x 160 x 44 mm
Von/Mit: Alan J. Izenman
Erscheinungsdatum: 28.08.2008
Gewicht: 1,429 kg
Artikel-ID: 101831098
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