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30 June 2021
31 December 2021
Functional and Topological Data Analysis
The journal "Statistics -- A Journal of Theoretical and Applied Statistics" announces a special issue on the topic "Functional and Topological Data Analysis". Authors are welcome and cordially encouraged to submit their innovative research contributions in this field to the journal.
The analysis of non-Euclidean data, i.e. observations which cannot be modeled as realizations of finite-dimensional random vectors with real-valued components, has gained increasing attention over the last ten to twenty years. Such statistical problems occur in the fields of functional data analysis (FDA), in which the data are viewed as realizations of random functions, as well as topological data analysis (TDA), in which e.g. data from topological groups are considered. These branches have been greatly motivated and influenced by the developments in computer science, which enable the processing of extremely high dimensional types of data, and by their applications in various fields of data-based science such as biometrics, econometrics, network data, machine learning etc. Topics in these areas include functional principal component analysis, nonparametric estimation and classification in FDA, functional time series, Frechet means, object-oriented data, statistical persistent homology, data on manifolds.
The general profile of the journal is more on the theoretical side. Thus we welcome contributions which yield e.g. novel asymptotic properties of
statistical procedures in FDA and TDA such as asymptotic normality, minimax optimality, adaptivity etc. But we also appreciate work with a stronger focus on applied or computational aspects of FDA and TDA. Such contributions will be considered for the special issue. However we do not publish pure case studies without any methodological or theoretical novelty. Please note that the contribution must be interesting to a statistical community.
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