A New Way of Analyzing Object Data from a Nonparametric Viewpoint
Nonparametric Statistics on Manifolds and Their Applications to Object Data Analysis provides one of the first thorough treatments of the theory and methodology for analyzing data on manifolds. It also presents in-depth applications to practical problems arising in a variety of fields, including statistics, medical imaging, computer vision, pattern recognition, and bioinformatics.
The book begins with a survey of illustrative examples of object data before moving to a review of concepts from mathematical statistics, differential geometry, and topology. The authors next describe theory and methods for working on various manifolds, giving a historical perspective of concepts from mathematics and statistics. They then present problems from a wide variety of areas, including diffusion tensor imaging, similarity shape analysis, directional data analysis, and projective shape analysis for machine vision. The book concludes with a discussion of current related research and graduate-level teaching topics as well as considerations related to computational statistics.
Researchers in diverse fields must combine statistical methodology with concepts from projective geometry, differential geometry, and topology to analyze data objects arising from non-Euclidean object spaces. An expert-driven guide to this approach, this book covers the general nonparametric theory for analyzing data on manifolds, methods for working with specific spaces, and extensive applications to practical research problems. These problems show how object data analysis opens a formidable door to the realm of big data analysis.
Nonparametric Statistics on Manifolds. Asymptotic Theory and Nonparametric Bootstrap on Special Manifolds. Applications in Object Data Analysis on Manifolds. Additional Topics.
"… the first extensive book on [this subject] … This book succeeds in unifying the field by bringing in disparate topics, already available in several papers, but not easy to understand, under one roof. … a brilliant and a bold idea by an active researcher, who is now joined in coauthorship by an enthusiastic, hardworking, and talented younger peer. … it exceeds all expectations, in particular regarding the extent to which complex differential geometric notions permeate statistics."
—From the Foreword by Victor Pambuccian, Professor of Mathematics, Arizona State University