Principal Component Analysis

Jolliffe, I.T.

ISBN 10 : 1441929991 ISBN 13 : 9781441929990

Editeur : Springer, 2010

A propos du livre

Revue de presse :

"Bringing the 1E up to date has added more than 200 pages of additional text. Anyone seriously involved with the application of PCA will certainly want to purchase a copy...Seldom has such a wealth of material on a single topic in statistics appeared in one book...All that material has gotten a whole lot more comprehensive here in this new edition. Goodall (1988) also labeled the book a good read. Now it may be a little heavy for that purpose, but it certainly is a fantastic reference book." --Technometrics

"This is the bible of principal component analysis (PCA). This second edition of the book is nearly twice the length of the first. [Short Book Reviews, Vol.6, p.45] New material includes discussion of ordination methods linked to PCA, including biplots, determining the number of components to retain, extended discussion of outlier detection, stability, and sensitivity, simplifying PCAs to aid interpretation, time series data, size/shape data, and nonlinear PCA, including the Gifi system and neural networks, and other topics. As can be seen from this, the book is not a narrow discussion of PCA, but links it effectively and in an illuminating way to a wide variety of other multivariate statistical tools. Principal component analysis is the empirical manifestation of the eigen value-decomposition of a correlation or covariance matrix. The fact that a book of nearly 500 pages can be written on this, and noting the author's comment that 'it is certain that I have missed some topics, and my coverage of others will be too brief for the taste of some readers' drives home the extent to whch statistics exceeds mere mathematics. This book is an invaluable reference work and I am pleased to have it on my shelves. My only regret is that I probably will not have time to read it from cover to cover with the attention it deserves." --ISI Short Book Reviews

"This book is one of the very few texts entirely devoted to principal component analysis (PCA). The second edition is usefully expanded and updated from the first edition; thus it is very well worth considering this edition, even if one is familiar with the first. ... Very nice features are the carefully discussed links between PCA and related techniques ... . Throughout, numerous references to relevant literature are provided. This book will be useful as an introduction to PCA as well as a reference." --Marieke E. Timmerman, Journal of the American Statistical Association, 2004

Présentation de l'éditeur :

Principal component analysis is central to the study of multivariate data. Although one of the earliest multivariate techniques it continues to be the subject of much research, ranging from new model- based approaches to algorithmic ideas from neural networks. It is extremely versatile with applications in many disciplines. The first edition of this book was the first comprehensive text written solely on principal component analysis. The second edition updates and substantially expands the original version, and is once again the definitive text on the subject. It includes core material, current research and a wide range of applications. Its length is nearly double that of the first edition. Researchers in statistics, or in other fields that use principal component analysis, will find that the book gives an authoritative yet accessible account of the subject. It is also a valuable resource for graduate courses in multivariate analysis. The book requires some knowledge of matrix algebra. Ian Jolliffe is Professor of Statistics at the University of Aberdeen. He is author or co-author of over 60 research papers and three other books. His research interests are broad, but aspects of principal component analysis have fascinated him and kept him busy for over 30 years.

À propos de cette édition

• Éditeur: Springer
• Date d'édition: 2010
• ISBN 10: 1441929991
• ISBN 13: 9781441929990
• Reliure: Livre broché
• Nombre de pages: 524