Statistical methodology and theory for functional and topological data. Abstracts from the workshop held June 16--22, 2019
type: Item
instance of: scholarly article
title: Statistical methodology and theory for functional and topological data. Abstracts from the workshop held June 16--22, 2019
zbMATH Open document ID: 1439.00059
DOI: 10.4171/OWR/2019/28
published in: Oberwolfach Reports
publication date: 2020-07-29T00:00:00Z
review text: Summary: The workshop focuses on the statistical analysis of complex data which cannot be represented as realizations of finite-dimensional random vectors. An example of such data are functional data. They arise in a variety of climate, biological, medical, physical and engineering problems, where the observations can be represented by curves and surfaces. Fast advances in technology continuously produce a deluge of bigger data with even more complex structures such as arteries in the brain, bones of a human body or social networks. This has sparked enormous interest in more general statistical problems where the random observations are elements of abstract topological spaces. The workshop will stimulate development of new statistical methods for these types of data and will be an ideal platform for discussing their theoretical properties (e.g. asymptotic optimality), computational performance, and new exciting applications in areas such as machine learning, image analysis, biometrics and econometrics.
Mathematics Subject Classification ID: 00B05
Mathematics Subject Classification ID: 00B25
Mathematics Subject Classification ID: 62-06
Mathematics Subject Classification ID: 62Hxx
Mathematics Subject Classification ID: 62Mxx
Mathematics Subject Classification ID: 62Rxx
zbMATH DE Number: 7225850
MaRDI profile type: Publication
cites work: CLT for linear spectral statistics of large-dimensional sample covariance matrices.
full work available at URL: https://doi.org/10.4171/owr/2019/28
OpenAlex ID: W3033901217
description: scientific article; zbMATH DE number 7225850