This is the first book on multivariate analysis to look at large data sets which describes the state of the art in analyzing such data. Material such as database management systems is included that has never appeared in statistics books before.

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. "For both applied and theoretical statisticians as well as investigators working in the many areas in which relevant use can be made of discriminant techniques, this monograph provides a modern, comprehensive, and systematic account of discriminant analysis, with the focus on the more recent advances in the field." –SciTech Book News ". . . a very useful source of information for any researcher working in discriminant analysis and pattern recognition." –Computational Statistics Discriminant Analysis and Statistical Pattern Recognition provides a systematic account of the subject. While the focus is on practical considerations, both theoretical and practical issues are explored. Among the advances covered are regularized discriminant analysis and bootstrap-based assessment of the performance of a sample-based discriminant rule, and extensions of discriminant analysis motivated by problems in statistical image analysis. The accompanying bibliography contains over 1,200 references.

Discriminant Analysis and Applications comprises the proceedings of the NATO Advanced Study Institute on Discriminant Analysis and Applications held in Kifissia, Athens, Greece in June 1972. The book presents the theory and applications of Discriminant analysis, one of the most important areas of multivariate statistical analysis. This volume contains chapters that cover the historical development of discriminant analysis methods; logistic and quasi-linear discrimination; and distance functions. Medical and biological applications, and computer graphical analysis and graphical techniques for multidimensional data are likewise discussed. Statisticians, mathematicians, and biomathematicians will find the book very interesting.

Background. Deriving the canonical discriminant functions. Interpreting the canonical discriminant functions. Classification procedures. Stepwise inclusion of variables. Concluding remarks.

A complete introduction to discriminant analysis--extensively revised, expanded, and updated This Second Edition of the classic book, Applied Discriminant Analysis, reflects and references current usage with its new title, Applied MANOVA and Discriminant Analysis. Thoroughly updated and revised, this book continues to be essential for any researcher or student needing to learn to speak, read, and write about discriminant analysis as well as develop a philosophy of empirical research and data analysis. Its thorough introduction to the application of discriminant analysis is unparalleled. Offering the most up-to-date computer applications, references, terms, and real-life research examples, the Second Edition also includes new discussions of MANOVA, descriptive discriminant analysis, and predictive discriminant analysis. Newer SAS macros are included, and graphical software with data sets and programs are provided on the book's related Web site. The book features: Detailed discussions of multivariate analysis of variance and covariance An increased number of chapter exercises along with selected answers Analyses of data obtained via a repeated measures design A new chapter on analyses related to predictive discriminant analysis Basic SPSS(r) and SAS(r) computer syntax and output integrated throughout the book Applied MANOVA and Discriminant Analysis enables the reader to become aware of various types of research questions using MANOVA and discriminant analysis; to learn the meaning of this field's concepts and terms; and to be able to design a study that uses discriminant analysis through topics such as one-factor MANOVA/DDA, assessing and describing MANOVA effects, and deleting and ordering variables.

The linear discriminant function; Discrete classification models; Error rates and the problem of bias; The variable-selection problem; Special topics; Computer programs.

Most books on discriminant analysis focus on statistical theory. But properly applied, discriminant analysis methods can be enormously useful in the interpretation of data. This book is the first ever to offer a complete introduction to discriminant analysis that focuses on applications. It provides numerous examples, explained in great detail, using current statistical discriminant analysis algorithms. It also develops several themes that will be useful to researchers and students regardless of the analytical methods they employ. They are the careful examination of data prior to final analysis; the application of critical judgment and common sense to all analyses and interpretations; and conducting multiple analyses as a matter of routine. To encourage and enable readers to conduct multiple analyses of their data, the accompanying diskette contains the four complete data sets and five special computer programs that are referred to repeatedly in the text and are the subjects of numerous exercise problems. This enables the reader to carry out package analyses on the data sets using a variety of procedural options both within and across computer packages. The term "discriminant analysis" means different things to different people. For statisticians and researchers in the physical sciences, it usually denotes the process through which group membership is predicted on the basis of multiple predictor variables. Behavioral scientists, on the other hand, often use discriminant analysis to describe group differences across multiple response variables. Though closely related, predictive discriminant analysis (PDA) and descriptive discriminant analysis (DDA) are used for different purposes and should be approached in different ways. To accentuate these differences and distinguish clearly between the two, Applied Discriminant Analysis presents these topics separately. For graduate students, this book will expand your background in multivariate data analysis methods and facilitate both the reading and the conducting of applied empirical research. It will also be of great use to experienced researchers who wish to enhance or update their quantitative background, and to methodologists who want to learn more about the details of applied discriminant data analysis, and some still unresolved problems, as well.