OBJECT ORIENTED DATA ANALYSIS

1. What is OODA?
2. Breadth of OODA
3. Data Object Definition
4. Exploratory and Confirmatory Analyses
5. OODA P6
6. Data Visualization
7. Distance Based Methods
8. Manifold Data Analysis
9. FDA Curve Registration
10. Graph Structured Data Objects
11. Classification - Supervised Learning
12. Clustering - Unsupervised Learning
13. High Dimensional Inference
14. High Dimensional Asymptotics
15. Smoothing and SiZer
16. Robust Methods
17. PCA Details and Variants
18. OODA Context and Related Areas

Object Oriented Data Analysis is a framework that facilitates inter-disciplinary research through new terminology for discussing the often many possible approaches to the analysis of complex data. Such data are naturally arising in a wide variety of areas. This book aims to provide ways of thinking that enable the making of sensible choices.

The main points are illustrated with many real data examples, based on the authors' personal experiences, which have motivated the invention of a wide array of analytic methods.

While the mathematics go far beyond the usual in statistics (including differential geometry and even topology), the book is aimed at accessibility by graduate students. There is deliberate focus on ideas over mathematical formulas.

J. S. Marron is the Amos Hawley Distinguished Professor of Statistics, Professor of Biostatistics, Adjunct Professor of Computer Science, Faculty Member of the Bioinformatics and Computational Biology Curriculum and Research Member of the Lineberger Cancer Center and the Computational Medicine Program, at the University of North Carolina, Chapel Hill. Ian L. Dryden is a Professor in the Department of Mathematics and Statistics at Florida International University in Miami, has served as Head of School of Mathematical Sciences at the University of Nottingham, and is joint author of the acclaimed book Statistical Shape Analysis.