Functional and Shape Data Analysis

Nonfiction, Science & Nature, Mathematics, Functional Analysis, Statistics
Cover of the book Functional and Shape Data Analysis by Eric P. Klassen, Anuj Srivastava, Springer New York
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Eric P. Klassen, Anuj Srivastava ISBN: 9781493940202
Publisher: Springer New York Publication: October 3, 2016
Imprint: Springer Language: English
Author: Eric P. Klassen, Anuj Srivastava
ISBN: 9781493940202
Publisher: Springer New York
Publication: October 3, 2016
Imprint: Springer
Language: English

This textbook for courses on function data analysis and shape data analysis describes how to define, compare, and mathematically represent shapes, with a focus on statistical modeling and inference. It is aimed at graduate students in analysis in statistics, engineering, applied mathematics, neuroscience, biology, bioinformatics, and other related areas. The interdisciplinary nature of the broad range of ideas covered—from introductory theory to algorithmic implementations and some statistical case studies—is meant to familiarize graduate students with an array of tools that are relevant in developing computational solutions for shape and related analyses. These tools, gleaned from geometry, algebra, statistics, and computational science, are traditionally scattered across different courses, departments, and disciplines; Functional and Shape Data Analysis offers a unified, comprehensive solution by integrating the registration problem into shape analysis, better preparing graduate students for handling future scientific challenges.

Recently, a data-driven and application-oriented focus on shape analysis has been trending. This text offers a self-contained treatment of this new generation of methods in shape analysis of curves. Its main focus is shape analysis of functions and curves—in one, two, and higher dimensions—both closed and open. It develops elegant Riemannian frameworks that provide both quantification of shape differences and registration of curves at the same time. Additionally, these methods are used for statistically summarizing given curve data, performing dimension reduction, and modeling observed variability. It is recommended that the reader have a background in calculus, linear algebra, numerical analysis, and computation.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

This textbook for courses on function data analysis and shape data analysis describes how to define, compare, and mathematically represent shapes, with a focus on statistical modeling and inference. It is aimed at graduate students in analysis in statistics, engineering, applied mathematics, neuroscience, biology, bioinformatics, and other related areas. The interdisciplinary nature of the broad range of ideas covered—from introductory theory to algorithmic implementations and some statistical case studies—is meant to familiarize graduate students with an array of tools that are relevant in developing computational solutions for shape and related analyses. These tools, gleaned from geometry, algebra, statistics, and computational science, are traditionally scattered across different courses, departments, and disciplines; Functional and Shape Data Analysis offers a unified, comprehensive solution by integrating the registration problem into shape analysis, better preparing graduate students for handling future scientific challenges.

Recently, a data-driven and application-oriented focus on shape analysis has been trending. This text offers a self-contained treatment of this new generation of methods in shape analysis of curves. Its main focus is shape analysis of functions and curves—in one, two, and higher dimensions—both closed and open. It develops elegant Riemannian frameworks that provide both quantification of shape differences and registration of curves at the same time. Additionally, these methods are used for statistically summarizing given curve data, performing dimension reduction, and modeling observed variability. It is recommended that the reader have a background in calculus, linear algebra, numerical analysis, and computation.

More books from Springer New York

Cover of the book Sustainable Web Ecosystem Design by Eric P. Klassen, Anuj Srivastava
Cover of the book Discoidin Domain Receptors in Health and Disease by Eric P. Klassen, Anuj Srivastava
Cover of the book Democratic Governance and Economic Performance by Eric P. Klassen, Anuj Srivastava
Cover of the book Remote Sensing by Eric P. Klassen, Anuj Srivastava
Cover of the book MHC Class I Antigens In Malignant Cells by Eric P. Klassen, Anuj Srivastava
Cover of the book Nonoscillation Theory of Functional Differential Equations with Applications by Eric P. Klassen, Anuj Srivastava
Cover of the book Handbook of Forensic Sociology and Psychology by Eric P. Klassen, Anuj Srivastava
Cover of the book Machine Tool Vibrations and Cutting Dynamics by Eric P. Klassen, Anuj Srivastava
Cover of the book Recovery of Gray Wolves in the Great Lakes Region of the United States by Eric P. Klassen, Anuj Srivastava
Cover of the book Sociophysiology by Eric P. Klassen, Anuj Srivastava
Cover of the book Hanging On and Letting Go by Eric P. Klassen, Anuj Srivastava
Cover of the book Molecular Mechanisms of Tumor Cell Resistance to Chemotherapy by Eric P. Klassen, Anuj Srivastava
Cover of the book Chaos in Switching Converters for Power Management by Eric P. Klassen, Anuj Srivastava
Cover of the book Handbook of Individual Differences in Cognition by Eric P. Klassen, Anuj Srivastava
Cover of the book SmartData by Eric P. Klassen, Anuj Srivastava
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy