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A User's Guide to Principal Componentsby J. Edward Jackson
John Wiley & Sons, Inc. 2005; US$ 208.00WILEY-INTERSCIENCE PAPERBACK SERIES The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. From the Reviews of A User’s Guide to Principal Components "The book is aptly and correctly named–A User’s Guide. It is the kind of book that a user at any level, novice or skilled practitioner, would want to have at hand for autotutorial, for refresher, or as a general-purpose guide through the maze of modern PCA." –Technometrics... more...
Multiple Correspondence Analysis and Related Methodsby Michael Greenacre
Taylor & Francis 2006; US$ 99.95Explaining the methodology step-by-step, this book offers a survey of the different approaches taken by researchers from different statistical "schools" and explores a variety of application areas. Each chapter includes empirical examples that provide a practical understanding of the method and its interpretation. more...
Factor Analysis at 100by Robert Cudeck
Lawrence Erlbaum Associates 2007; US$ 42.95Factor analysis is one of the success stories of statistics in the social sciences. It provides a way to investigate latent variables, the fundamental traits and concepts in the study of individual differences. This book demonstrates how and why factor analysis is considered to be one of the methodological pillars of behavioral research. more...
Correspondence Analysis in Practiceby Michael Greenacre
Taylor & Francis 2007; US$ 79.95Drawing on the author's experience in social and environmental research, Correspondence Analysis in Practice, Second Edition shows how the versatile method of correspondence analysis (CA) can be used for data visualization in a wide variety of situations. This completely revised, up-to-date edition features a didactic approach with self-contained chapters, extensive marginal notes, informative figure and table captions, and end-of-chapter summaries. New to this Second Edition include: five new chapters on transition and regression relationships, stacked tables, subset correspondence analysis, analysis of square tables, and canonical correspondence analysis; substantially more figures and tables than the first edition; and, a computational... more...
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Springer 2007; US$ 129.00In 1901, Karl Pearson invented Principal Component Analysis (PCA). Since then, PCA serves as a prototype for many other tools of data analysis, visualization and dimension reduction: Independent Component Analysis (ICA), Multidimensional Scaling (MDS), Nonlinear PCA (NLPCA), Self Organizing Maps (SOM), etc. The book starts with the quote of the classical Pearson definition of PCA and includes reviews of various methods: NLPCA, ICA, MDS, embedding and clustering algorithms, principal manifolds and SOM. New approaches to NLPCA, principal manifolds, branching principal components and topology preserving mappings are described as well. Presentation of algorithms is supplemented by case studies, from engineering to astronomy, but mostly of biological... more...
Statistical Factor Analysis and Related Methodsby Alexander T. Basilevsky
John Wiley & Sons, Inc. 2009; US$ 132.00Statistical Factor Analysis and Related Methods Theory and Applications In bridging the gap between the mathematical and statistical theory of factor analysis, this new work represents the first unified treatment of the theory and practice of factor analysis and latent variable models. It focuses on such areas as: * The classical principal components model and sample-population inference * Several extensions and modifications of principal components, including Q and three-mode analysis and principal components in the complex domain * Maximum likelihood and weighted factor models, factor identification, factor rotation, and the estimation of factor scores * The use of factor models in conjunction with various types of data including time series,... more...
Applied Factor Analysis in the Natural Sciencesby Richard A. Reyment; K. G. Jvreskog
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