Nonparametric Statistics on Stratified Spaces and Their Applications in Object Data Analysis

— —

Victor Patrangenaru (Florida State University, Tallahassee, USA) Daniel E. Osborne (Florida Agricultural and Mechanical University, U.S.A)

Nonparametric Statistics on Stratified Spaces and Their Applications in Object Data Analysis

Victor Patrangenaru (Florida State University, Tallahassee, USA), Daniel E. Osborne (Florida Agricultural and Mechanical University, U.S.A)

$252

Hardback

Forthcoming
Pre-Order now

QTY:

English

Routledge
22 July 2025

Probability & statistics; Imaging systems & technology

This book, a follow-up of Nonparametric Statistics on Manifolds and Their Applications to Object Data Analysis where the reader was introduced to data analysis on manifolds, is expanding the subject of data analysis on object spaces, to the case of metric spaces that have a smooth structure on an open dense subset only, governed by a nice filtration structure on the singular part of this space, that is comprised of manifolds whose boundaries are made of lower dimensional manifolds in this filtration. Such object spaces are known as stratified spaces, and their structure will be detailed in the second part of the book. Key examples of complex data from which one extracts data representable as points on a stratified space, and a review on data analysis on manifolds, are provided, alongside a summary of results on nonparametric methods on manifolds. Key results on asymptotic and nonparametric bootstrap on some stratified spaces are included. Certain object spaces with a manifold stratification arising in Statistics are considered with examples of application on data analysis and on them. Various applications include RNA based analysis of the SARSCov2 virus, 3D face identification from digital camera images, English Alphabet based comparison of certain European Languages. Nonparametric Statistics on Stratified Spaces and Their Applications in Object Data Analysis is intended for graduate students to expand their understanding of Nonparametric Statistics, and enhance their ability to grasp and extract complex data and analysis of manifolds.

By: Victor Patrangenaru (Florida State University Tallahassee USA), Daniel E. Osborne (Florida Agricultural and Mechanical University, U.S.A)
Imprint: Routledge
Country of Publication: United Kingdom
Dimensions: Height: 234mm, Width: 156mm,
Weight: 453g
ISBN: 9781138043138
ISBN 10: 1138043133
Pages: 208
Publication Date: 22 July 2025
Audience: College/higher education , Primary
Format: Hardback
Publisher's Status: Forthcoming

Foreword Preface Part 1: Data and Preliminares for Analysis on Stratified Spaces 1. Data of Complex Type 2. Review of Nonparametric Statistics on Manifolds Part 2: Nonparametric Statistics on Stratified Spaces 3. Spaces with a Manifold Stratification 4. Extrinsic Data Analysis on Stratified Spaces 5. Intrinsic Sample Means on Tree Spaces 6. Central Limit Theorem for Random Samples on a Graph 7. Analysis of Magnetic Resonance Angiography Data 8. An Application to Phylogenies of SARS-CoV-2 Data Analysis Part 3: Asymptotic Theory and Nonparametric Bootstrap on Special Stratified Spaces 9. CLT on Low Dimensional Stratified Spaces 10. Investigating Two Possible Origins of SARS-CoV-2 11. Applications of Tree Spaces to Language Ancestry 12. 3D Face Differentiation from Digital Camera Images 13. Further Directions in Statistics on Stratified Spaces Bibliography

Vic Patrangenaru is a professor in the Statistics Department at Florida State University, Tallahassee, Florida, USA. He is an honored fellow of the Institute of Mathematical Statistics. His research encompasses analysis of complex data types, and the application of projective and differential geometry in various fields, including computer vision, medical imaging and phylogenetics. Throughout his career Dr. Patrangenaru, who guided many doctoral students, spearheaded the new area of Object Data Analysis. He added a new class to the Mathematics Subject Classification 2020 , 62R30 Statistics on Manifolds, which is currently in use by Mathematical Reviews and Zentralblatt für Mathematik Daniel E. Osborne is an associate professor in the Mathematics Department at Florida A&M University, Tallahassee, Florida, USA. As a trained Statistician and Data Science educator, he is dedicated to training student learners and promoting best practices in data literacy, statistical reasoning, data analysis, and data visualization skills among all learners, irrespective of their backgrounds, majors, or career paths.