Modern Multivariate Statistical Techniques / Springer Texts in Statistics (PDF)

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)