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.

Table of Contents

What is OODA?

Breadth of OODA

Data Object Definition

Exploratory and Confirmatory Analyses

OODA P6

Data Visualization

Distance Based Methods

Manifold Data Analysis

FDA Curve Registration

Graph Structured Data Objects

Classification - Supervised Learning

Clustering - Unsupervised Learning

High Dimensional Inference

High Dimensional Asymptotics

Smoothing and SiZer

Robust Methods

PCA Details and Variants

OODA Context and Related Areas