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This book 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 authors introduce the relevant background material from statistics and differential geometry and explore ongoing research areas. Numerous exercises reinforce readers' understanding of the underlying theoretical concepts.

Produktbeschreibung
This book 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 authors introduce the relevant background material from statistics and differential geometry and explore ongoing research areas. Numerous exercises reinforce readers' understanding of the underlying theoretical concepts.

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Autorenporträt
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.