
- 424 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
Object Oriented Data Analysis
About this book
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.
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Information
Table of contents
- Cover Page
- Half-Title Page
- Series Page
- Title Page
- Copyright Page
- Dedication Page
- Contents
- Preface
- 1 What Is OODA?
- 2 Breadth of OODA
- 3 Data Object Definition
- 4 Exploratory and Confirmatory Analyses
- 5 OODA Preprocessing
- 6 Data Visualization
- 7 Distance Based Methods
- 8 Manifold Data Analysis
- 9 FDA Curve Registration
- 10 Graph Structured Data Objects
- 11 ClassificationāSupervised Learning
- 12 ClusteringāUnsupervised 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
- Bibliography
- Index