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.

chapter Chapter 1|18 pages

What Is OODA?

chapter Chapter 2|12 pages

Breadth of OODA

chapter Chapter 3|16 pages

Data Object Definition

chapter Chapter 4|24 pages

Exploratory and Confirmatory Analyses

chapter Chapter 5|26 pages

OODA Preprocessing

chapter Chapter 6|28 pages

Data Visualization

chapter Chapter 7|22 pages

Distance Based Methods

chapter Chapter 8|28 pages

Manifold Data Analysis

chapter Chapter 9|22 pages

FDA Curve Registration

chapter Chapter 10|18 pages

Graph Structured Data Objects

chapter Chapter 11|28 pages

Classification–Supervised Learning

chapter Chapter 12|14 pages

Clustering–Unsupervised Learning

chapter Chapter 13|18 pages

High-Dimensional Inference

chapter Chapter 14|18 pages

High Dimensional Asymptotics

chapter Chapter 15|20 pages

Smoothing and SiZer

chapter Chapter 16|18 pages

Robust Methods

chapter Chapter 17|30 pages

PCA Details and Variants

chapter Chapter 18|10 pages

OODA Context and Related Areas