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Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning (Springer Texts in Statistics) Hardcover – March 11, 2013

ISBN-13: 978-0387781884 ISBN-10: 0387781889 Edition: 1st ed. 2008. Corr. 2nd printing 2013

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Product Details

  • Series: Springer Texts in Statistics
  • Hardcover: 733 pages
  • Publisher: Springer; 1st ed. 2008. Corr. 2nd printing 2013 edition (March 11, 2013)
  • Language: English
  • ISBN-10: 0387781889
  • ISBN-13: 978-0387781884
  • Product Dimensions: 9.3 x 5.9 x 1.5 inches
  • Shipping Weight: 2.6 pounds (View shipping rates and policies)
  • Average Customer Review: 4.6 out of 5 stars  See all reviews (8 customer reviews)
  • Amazon Best Sellers Rank: #126,356 in Books (See Top 100 in Books)

Editorial Reviews

Review

From 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)

“This monograph provides a comprehensive account of the development of multivariate statistical analysis powered by the explosion in the capability and speed of computers during the last four decades. It is written by an expert in the field. The book is suitable for very advanced undergraduate students and graduate students in statistics, but can also be used in a host of other areas … where statistics plays a major role. … Any researcher in multivariate statistical analysis should have this book in his personal library.” (Steen Arne Andersson, Mathematical Reviews, Issue 2010 b) “…Exemplifies the transition of statistical science as a scientific discipline focused on testing to one focused on information and knowledge discovery. …Acknowledges in a novel way the link between statistical science and computer science, artificial intelligence, and machine learning theory…This book implements an overhaul for teaching multivariate analysis…” (The American Statistician, February 2010, Vol. 64 No.1)

“The author of this well-written, encyclopaedic text of roughly 730 pages highlights data mining using huge data sets and aims to blend ‘classical’ multivariate topics (such as regression, principal components and linear discriminant analysis, clustering, multi-dimensional scaling and correspondence analysis) with more recent advances from the field of computational statistics (such as classification and regression trees, neural networks, support vector machines or topics around committee machines—bagging, boosting and random forests). It is noteworthy that some of the more classical methods are derived as special cases of a common theoretical framework: reduced rank regression, a field to which Professor Izenman already has contributed with his doctoral thesis back in 1972. …Furthermore it is worth noting as well that the first chapter after the introductory overview deals with data, databases and database management—indicating the author’s seriousness about data analysis in the presence of permanently growing magnitudes of data sets to analyse. …Most chapters end with sections on software packages, and all chapters end with bibliographical notes and exercises; the final list of references contains 552 entries. …Personally, I felt the book to be heavy, yet rewarding, reading. It seems to have full potential to become a second standard reference next to Hastie et al. (2009).” (Journal of the Royal Statistical Society)

“In Modern Multivariate Statistical Techniques, Alan Izenman attempts to synthesize multivariate methods developed across the various literatures into a comprehensive framework. The goal is to present the current state of the art  in multivariate analysis methods while attempting to place them on a firm statistical basis. …This book would be a fantastic reference for researchers interested in learning about multivariate and machine learning methods. …The first half of the book would be suitable for an advanced undergraduate or graduate multivariate analysis course. The second half of the book would be a great reference for a machine-learning course. I definitely enjoyed reading the book.”  (Biometrics, Summer 2009, 65, 990–991)

“This remarkable book exposes a wide range of techniques from the ‘statistical learning’ perspective. It is addressed to readers with a background in probability, statistical theory, multivariate calculus, linear algebra and notions of Bayesian methods. … The exercises at the end of each chapter propose both theoretical derivations and practical work with real data. … It can be used as a basis for different advanced courses. The first chapters can be employed for an introduction to modern prediction methods.” (Ricardo Maronna, Statistical Papers, Vol. 52, 2011)

From the Back Cover

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.

Alan J. Izenman is Professor of Statistics and Director of the Center for Statistical and Information Science at Temple University. He has also been on the faculties of Tel-Aviv University and Colorado State University, and has held visiting appointments at the University of Chicago, the University of Minnesota, Stanford University, and the University of Edinburgh. He served as Program Director of Statistics and Probability at the National Science Foundation and was Program Chair of the 2007 Interface Symposium on Computer Science and Statistics with conference theme of Systems Biology. He is a Fellow of the American Statistical Association.

  


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Customer Reviews

4.6 out of 5 stars
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In fact most of these topics are avoided.
Michael R. Chernick
Overall, this is a wonderful survey of a wide range of multivariate techniques and methods.
Robert S. Newman
Probably it's my own damn fault for not figuring this out.
shanusmagnus

Most Helpful Customer Reviews

22 of 22 people found the following review helpful By Robert S. Newman on February 6, 2009
Format: Hardcover Verified Purchase
This book surprised me. I was expecting a book filled with a discussion of mostly traditional multivariate techniques supplemented by a few chapters of more recent developments. Instead, I found a completely new and refreshing approach to statistics and data exploration that framed the classical regression approach to most issues as a special, limiting case of a broader view of data exploration and analysis.

Sections on random vectors and matrices, nonparametric density estimation, tree methods, ANI, support vector machines, random forests, bagging and boosting, latent variables, manifold learning, and other topics are discussed and explored in adequate depth for an introductory text. The book assumes you know matrix algebra and have had some exposure to probability distributions, and common multivariate methods, but it extends the discussion in areas that are usually only covered in separate advanced texts and research papers.

The book is a little light on Bayesian methods but some compromises had to be made considering the bulk of the range of new material discussed. I especially liked the broad array of examples from genetics, medicine, physics, and other application areas and the nice color graphs where needed. The references to Matlab, R, S-Plus and other standard math packages was much appreciated although I would have liked Mathematica to have been included as well.

Overall, this is a wonderful survey of a wide range of multivariate techniques and methods. I hope it gets incorporated in college grad and undergrad courses.
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11 of 11 people found the following review helpful By Michael R. Chernick on October 12, 2010
Format: Hardcover
Traditional graduate level texts such as Ted Anderson's focus on the multivariate normal distribution and its statistical properties. So out of that we get MANOVA, Hotelling's T square, linear and quadratic discriminant analysis, principal component analysis, Wishart distributions and canonical correlations. As other reviewers have said this book is quite different. You don't see those topics as chapters in this book. In fact most of these topics are avoided. Izenman finds that with large dimensional data sets that come up in practice these classical techniques do not work very well. So he takes a more modern and "nonparametric' approach. Color adds to the attractiveness of the book although often not essential to the graphical description of the data.

The book begins with exploratory data analysis and extends it to the realm of data mining. The ability to do analysis like this on large data sets comes from the amazing advances in computer speed. Several important concepts are introduced in intuitive ways including pattern recognition and machine learning, prediction error, cross-validation and bootstrap, and overfitting of models.

Chapter is again aimed at the practical by emphaiszing data structure and data bases and by introducing data quality issues including data inconsistencies, outlying observations (which becomes more complicated in multivariate analysis as many directions in a multivariate space can be considered extreme), missing data, and common to today's research data containing many variables but only a few observations such as gene expression on microarrays and satellite images.

But great ideas are not always modern. Izenman points to the curse of dimensionality, a concept coined by Richard Belman back in 1961.
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10 of 12 people found the following review helpful By Statistixian on September 5, 2009
Format: Hardcover
This is not Johnson and Wichern or TW Anderson - Think Bishop (PRML) or Hastie, Tibshirani and Friedman (EoSL). We used this for a course last year and this is a great book - as opposed to Bishop which treats things form a Com. Sci. perspective or HTF which assumes a much higher level. One warning though - don't be turned off by the multivariate notation (Duh... Look at the title, of course), but once you master the early chapter on matrix theory and analysis, everything else is very readable.
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Format: Hardcover
An excellent introduction to what should be standard statistical methods throughout the social, behavioral, cognitive and other sciences. The breadth of approaches and the number of techniques introduced is impressive. Alas, an obvious trade-off is the depth of the treatment possible for any single one, but the author strikes a nice balance. The only deficit is chapter 3, which should be included (if at all) as an appendix. It is a review of matrix/linear algebra that is too brief to impart anything useful to any not familiar with the subject enough to make the review needless, excepting perhaps for particular topics (which is why many books include such sections as an appendix). As a separate chapter, it breaks the flow and likely intimidates those not as confident about their mathematical acumen and knowledge by introducing the reader to an enormously broad subject matter involving some fairly sophisticated undergraduate level mathematics (or graduate level for most in the social/behavioral-type sciences) in so summary a fashion. Better the notation be introduced before the first chapter and the review material left as an appendix. Other than that, it is a must read for any who want to know what modern statistics could and should involve.
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