Abstract
The utilization of statistical methods an their applications within the new field of study known as Topological Data Analysis has has tremendous potential for broadening our exploration and understanding of complex, high-dimensional data spaces. This paper provides an introductory overview of the mathematical underpinnings of Topological Data Analysis, the workflow to convert samples of data to topological summary statistics, and some of the statistical methods developed for performing inference on these topological summary statistics. The intention of this non-technical overview is to motivate statisticians who are interested in learning more about the subject.
Keywords
Topological Data Analysis, Persistent Homology, Persistence Diagrams, Barcodes, Persistence Landscapes, Statistics
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Recommended Citation
Medina, Patrick S. and Doerge, R W.
(2015).
"Statistical Methods in Topological Data Analysis for Complex, High-Dimensional Data,"
Conference on Applied Statistics in Agriculture.
https://doi.org/10.4148/2475-7772.1130
Statistical Methods in Topological Data Analysis for Complex, High-Dimensional Data
The utilization of statistical methods an their applications within the new field of study known as Topological Data Analysis has has tremendous potential for broadening our exploration and understanding of complex, high-dimensional data spaces. This paper provides an introductory overview of the mathematical underpinnings of Topological Data Analysis, the workflow to convert samples of data to topological summary statistics, and some of the statistical methods developed for performing inference on these topological summary statistics. The intention of this non-technical overview is to motivate statisticians who are interested in learning more about the subject.
