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
General
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