Librería Portfolio

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.

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