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Multivariate Data Reduction and Discrimination with SAS Software (Anglais) Broché – 25 mai 2000


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Revue de presse

"...a core or supplementary text for graduate or senior undergraduate students, and a reference for researchers and practitioners." (SciTech Book News, Vol. 24, No. 4, December 2000) --Ce texte fait référence à l'édition Broché .

Présentation de l'éditeur

Multivariate data commonly encountered in a variety of disciplines is easy to understand with the approaches and methods described in Multivariate Data Reduction and Discrimination with SAS Software. Authors Ravindra Khattree and Dayanand Naik present the conceptual developments, theory, methods, and subsequent data analyses systematically and in an integrated manner. The data analysis is performed using many multivariate analysis components available in SAS software. Illustrations are provided using an ample number of real data sets drawn from a variety of fields, and special care is taken to explain the SAS codes and the interpretation of corresponding outputs. As a companion volume to the authors' previous book, Applied Multivariate Analysis with SAS Software, which discusses multivariate normality-based analyses, this book covers topics where, for the most part, assuming multivariate normality (or any other distributional assumption) is not crucial. Since the techniques discussed in this book also form the foundation of data mining methodology, the book will be of interest to data mining practitioners.


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Couverture | Copyright | Table des matières | Extrait | Index | Quatrième de couverture
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28 internautes sur 29 ont trouvé ce commentaire utile 
good practical book on data reduction and discrimination 27 juin 2008
Par Michael R. Chernick - Publié sur Amazon.com
Format: Broché
I know these authors very well and they are both good instructors and writers. The nice thing about their two books is that they cover the important material for applied statisticians and they illustrate the techniques with SAS programming. This is especially helpful for those of us working in the pharmaceutical industry where SAS is essentially mandated.

The only reason I gave it 4 stars instead of 5 is that they still recommend the leave-one-out method for error estimation in discriminant analysis when the work of Efron and others in the mid 1980s indicated that bootstrap methods of bias adjustment are generally superior to leave-one-out. The authors neglected to mention anything about the bootstrap literature on it.
15 internautes sur 15 ont trouvé ce commentaire utile 
Good general introduction to standard methods 11 janvier 2007
Par Dr. Lee D. Carlson - Publié sur Amazon.com
Format: Broché Achat vérifié
Written for seasoned users of SAS and statistical modelers, this book gives a fine overview of how some of the more important tools in dimensional reduction and multivariate analysis can be implemented in SAS. It is full of examples, with the SAS source code included, and can be used in a cookbook fashion with the appropriate material drawn as needed. This reviewer did not read the book from cover to cover, and did not test all the SAS source code that has been provided, but found the book helpful in areas such as principal component analysis, discrimination analysis, and cluster analysis. The only issue of concern that might be of concern, depending of course on one's particular philosophy of programming, is that the SAS coding is not approached from the standpoint of canned routines or macros. Some individuals who program in SAS, such as this reviewer, prefer that macros be used as much as possible in order to encapsulate just what the code is supposed to do. This approach is also more appropriate in a professional programming environment where it is imperative that the code be trustworthy and reliable. The SAS code in the book that this reviewer actually used does do its job but would need refinement if used in a commercial environment or financial institution where model risk is an important factor. In addition, those who have access to SAS Enterprise Guide will note that it contains SAS routines that can do many of the same things that are outlined in this book, and without detailed expertise in SAS. Many people have also used Enterprise Guide, and any bugs in it have therefore been ameliorated. This makes it trustworthier in a professional programming environment.

There are also some omissions in the book whose inclusion may have been helpful for the more advanced modeler/SAS programmer. The chapter on cluster analysis for example does not include a discussion on self-organizing maps or general competitive learning algorithms. Kernel-based methods, such as support vector machines are also not discussed in the book, as well as regression trees and neural networks. These used to be considered exotic or esoteric subjects for statistical modelers, but in the last few years the use of neural networks and support vector machines is becoming routine, especially in image analysis, computational radiology, and financial modeling. The book would have been awesome if these topics were discussed, along with inclusion of the appropriate SAS source code.
1 internautes sur 1 ont trouvé ce commentaire utile 
large statistics packages 19 juillet 2005
Par W Boudville - Publié sur Amazon.com
Format: Broché
SAS Institute has built up a huge portfolio of statistical packages. This book lets you appreciate a portion of it. Directed at a reader who is a professional statistician. Or perhaps a programmer. Preferably both, maybe.

The statistical terminology was mostly unfamiliar to me, and I've had a couple of years of undergraduate stats classes. Thus, the bulk of the book was over my head. But it should be said that the SAS code performs many of these functions, leaving you to concentrate on the higher level, manual assessment of the results. What the book shows you is essentially how to save your time, by avoiding having to code that functionality yourself.
0 internautes sur 1 ont trouvé ce commentaire utile 
As Described - New Book 3 février 2010
Par Kelvin Nham - Publié sur Amazon.com
Format: Broché Achat vérifié
Order it during Holiday season Christmas and New Year so it took a bit longer to come but i assume it is not their fault for all the holiday off the post office is taking. Would buy again for the quality of the item.
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