Recent Advances in Descriptive Multivariate Analysis [Hardcover]

This book provides a state of the art summary on the general theme of descriptive multivariate analysis. It consists of a collection of commissioned, edited articles by an international group of leading researchers: Phipps Arabie (Rutgers University) writes on Clustering from the Perspective of Combinatorial Data Analysis . Bernard Flury (Indiana University) includes some highly novel and previously unpublished work in Principal Component Models for Patterned Covariance Matrices . David Edwards' (University of Novi Nordisk, Denmark) Graphical Modelling and the chapter on Convergent Computation provide thorough and useful surveys of recent research not available in other texts on multivariate analysis. Other contributions include James Ramsay (McGill University, Montreal); Clause Wiehs (CIBA-GEIGY, Bosle); Willen Heiser (University of Leiden); and Ruben Gabriel (University of Rochester, New York). The material should provide a useful reference for graduate students and researchers.

Foreword
1. Clustering from the perspective of combinatorial data analysis
2. Developments in principal component analysis
3. Canonical discriminant analysis: comparison of resampling methods and convex-hull approximation
4. Nonlinear methods for the analysis of homogeneity and heterogeneity
5. Principles component models for patterned covariance matrices, with applications to canonical correlation analysis of several sets of variables
6. Orthogonal and projection Procrustes analysis
7. Graphical Modelling
8. Convergent computation by iterative majorization: theory and applications in multidimensional data analysis
9. Biplot display of multivariate categorical data, with comments on multiple correspondence analysis
10. MANOVA biplots for two-way contingency tables
11. Some tools for the multivariate analysis of functional data
12. A general theory of biplots
References

It fills a gap left by standard textbooks on multivariatelCÃ