Nonparametric Statistics on Manifolds and Their Applications to Object Data Analysis
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"… 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
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.
Les mer
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 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.
Detaljer
- Forlag
- CRC Press
- Innbinding
- Paperback
- Språk
- Engelsk
- Sider
- 517
- ISBN
- 9780367737825
- Utgivelsesår
- 2020
- Format
- 23 x 16 cm
Om forfatteren
Victor Patrangenaru is a professor of statistics at Florida State University. He received his first PhD from the University of Haifa; his differential geometry dissertation on locally homogeneous Riemannian and pseudo-Riemannian manifolds was conferred the Morris Pulver award. His second PhD was conferred at Indiana University for his dissertation on asymptotic statistics on manifolds and their applications. He has been a recipient of the Rothrock Mathematics Teaching Award from Indiana University.
Leif Ellingson is an assistant professor at Texas Tech University. He received his PhD in statistics from Florida State University; his dissertation "Statistical Shape Analysis on Manifolds with Applications to Planar Contours and Structural Proteomics" received the Ralph A. Bradley award. He has also been a recipient of the New Faculty Award from the Texas Tech Alumni Association. His current research interests include nonparametric statistics on manifolds, shape analysis, computational methods in statistics, and utilizing statistical methods in structural proteomics.
Leif Ellingson is an assistant professor at Texas Tech University. He received his PhD in statistics from Florida State University; his dissertation "Statistical Shape Analysis on Manifolds with Applications to Planar Contours and Structural Proteomics" received the Ralph A. Bradley award. He has also been a recipient of the New Faculty Award from the Texas Tech Alumni Association. His current research interests include nonparametric statistics on manifolds, shape analysis, computational methods in statistics, and utilizing statistical methods in structural proteomics.
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«
"… 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