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Mathematics
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Geometry and Topology in Statistics
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Geometry and Topology in Statistics

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Computational and Applied Mathematics".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 7153

Special Issue Editor

Dr. Stéphane Puechmorel

E-Mail Website
Guest Editor
Laboratoire de Mathématiques Appliquées, Ecole Nationale de l'Aviation Civile, 7 Av. Edouard Belin, 31400 Toulouse, France
Interests: information geometry; dual connections; gauge structures; foliations; differential geometry applied to machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

One can expect that the development of statistical geometry will be a major trend in the next decade. Due to numerous applications in data analysis, artificial intelligence and signal processing, along with a strong potential for new theoretical results, this emerging axis of research is of increasing interest among both the geometry and statistics communities.

This Special Issue aims at publishing papers of high-quality related to information geometry, geometric statistics, topology for learning and data analysis.

Some of the scientific topics of interest may include, but are not limited to: probabilities on manifolds, shape space, optimal transport, statistical manifolds, geometry of graphs, topology in data, geometry in artificial intelligence, estimation and sampling in high dimensions.

Submissions describing applications are welcome.

Prof. Dr. Stéphane Puechmorel
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • information geometry
  • topological data analysis
  • hessian geometry
  • shape space
  • optimal transport

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Published Papers (3 papers)

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Research

10 pages, 2125 KiB  
Article
Topology Adaptive Graph Estimation in High Dimensions
by Johannes Lederer and Christian L. Müller
Mathematics 2022, 10(8), 1244; https://doi.org/10.3390/math10081244 - 10 Apr 2022
Viewed by 1654
Abstract
We introduce Graphical TREX (GTREX), a novel method for graph estimation in high-dimensional Gaussian graphical models. By conducting neighborhood selection with TREX, GTREX avoids tuning parameters and is adaptive to the graph topology. We compared GTREX with standard methods on a new simulation [...] Read more.
We introduce Graphical TREX (GTREX), a novel method for graph estimation in high-dimensional Gaussian graphical models. By conducting neighborhood selection with TREX, GTREX avoids tuning parameters and is adaptive to the graph topology. We compared GTREX with standard methods on a new simulation setup that was designed to assess accurately the strengths and shortcomings of different methods. These simulations showed that a neighborhood selection scheme based on Lasso and an optimal (in practice unknown) tuning parameter outperformed other standard methods over a large spectrum of scenarios. Moreover, we show that GTREX can rival this scheme and, therefore, can provide competitive graph estimation without the need for tuning parameter calibration. Full article
(This article belongs to the Special Issue Geometry and Topology in Statistics)
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15 pages, 266 KiB  
Article
Lifting Dual Connections with the Riemann Extension
by Stéphane Puechmorel
Mathematics 2020, 8(11), 2079; https://doi.org/10.3390/math8112079 - 21 Nov 2020
Cited by 3 | Viewed by 2082
Abstract
Let (M,g) be a Riemannian manifold equipped with a pair of dual connections (∇,∇*). Such a structure is known as a statistical manifold since it was defined in the context of information geometry. [...] Read more.
Let (M,g) be a Riemannian manifold equipped with a pair of dual connections (∇,∇*). Such a structure is known as a statistical manifold since it was defined in the context of information geometry. This paper aims at defining the complete lift of such a structure to the cotangent bundle T*M using the Riemannian extension of the Levi-Civita connection of M. In the first section, common tensors are associated with pairs of dual connections, emphasizing the cyclic symmetry property of the so-called skewness tensor. In a second section, the complete lift of this tensor is obtained, allowing the definition of dual connections on TT*M with respect to the Riemannian extension. This work was motivated by the general problem of finding the projective limit of a sequence of a finite-dimensional statistical manifold. Full article
(This article belongs to the Special Issue Geometry and Topology in Statistics)
13 pages, 323 KiB  
Article
Diffeological Statistical Models, the Fisher Metric and Probabilistic Mappings
by Hông Vân Lê
Mathematics 2020, 8(2), 167; https://doi.org/10.3390/math8020167 - 30 Jan 2020
Cited by 3 | Viewed by 2226
Abstract
We introduce the notion of a C k -diffeological statistical model, which allows us to apply the theory of diffeological spaces to (possibly singular) statistical models. In particular, we introduce a class of almost 2-integrable C k -diffeological statistical models that encompasses all [...] Read more.
We introduce the notion of a C k -diffeological statistical model, which allows us to apply the theory of diffeological spaces to (possibly singular) statistical models. In particular, we introduce a class of almost 2-integrable C k -diffeological statistical models that encompasses all known statistical models for which the Fisher metric is defined. This class contains a statistical model which does not appear in the Ay–Jost–Lê–Schwachhöfer theory of parametrized measure models. Then, we show that, for any positive integer k , the class of almost 2-integrable C k -diffeological statistical models is preserved under probabilistic mappings. Furthermore, the monotonicity theorem for the Fisher metric also holds for this class. As a consequence, the Fisher metric on an almost 2-integrable C k -diffeological statistical model P ⊂ P ( X ) is preserved under any probabilistic mapping T : X ⇝ Y that is sufficient w.r.t. P. Finally, we extend the Cramér–Rao inequality to the class of 2-integrable C k -diffeological statistical models. Full article
(This article belongs to the Special Issue Geometry and Topology in Statistics)
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