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Statistical Learning of Networks and Functional Data

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 2144

Special Issue Editor


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Guest Editor
Laboratoire de Mathématiques et Applications, University of Poitiers, 86000 Poitiers, France
Interests: nonparametric statistics; stochastic algorithms; statistics applied to life sciences; statistical learning of networks; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Databases are becoming more and more accessible, voluminous and complex. In order to make the best use of them, non-parametric statistical methods, stochastic algorithms, statistical learning of networks are frequently used.

There has been growing increasing interest in functional data analysis and statistical learning of networks, including correlation analyses for spatial and temporal data and classification techniques for complex data. Progress has often been driven by the application areas, such as neurosciences, environmetrics, chemometrics, biometrics, medicine, and econometrics.

The application of functional data and statistical learning of networks to data of real-world complex systems are often hindered by the frequent lack of the convergence problems and sufficient asymptotic mathematical properties. Contributions addressing any of these issues are very welcome.

This Special Issue aims to be a forum for the presentation of new and improved techniques in the area of functional data and statistical learning of networks. In particular, the analysis and interpretation of real-world natural and engineered complex systems with the help of non-parametric statistical methods, stochastic algorithms, statistical learning of networks, functional data fall within the scope of this Special Issue.

Prof. Dr. Yousri Slaoui
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. Entropy is an international peer-reviewed open access monthly 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

  • nonparametric statistics
  • stochastic algorithms
  • statistics applied to life sciences
  • statistical learning of networks
  • artificial intelligence

Published Papers (1 paper)

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Research

14 pages, 867 KiB  
Article
Semi-Parametric Estimation Using Bernstein Polynomial and a Finite Gaussian Mixture Model
by Salima Helali, Afif Masmoudi and Yousri Slaoui
Entropy 2022, 24(3), 315; https://doi.org/10.3390/e24030315 - 23 Feb 2022
Cited by 3 | Viewed by 1553
Abstract
The central focus of this paper is upon the alleviation of the boundary problem when the probability density function has a bounded support. Mixtures of beta densities have led to different methods of density estimation for data assumed to have compact support. Among [...] Read more.
The central focus of this paper is upon the alleviation of the boundary problem when the probability density function has a bounded support. Mixtures of beta densities have led to different methods of density estimation for data assumed to have compact support. Among these methods, we mention Bernstein polynomials which leads to an improvement of edge properties for the density function estimator. In this paper, we set forward a shrinkage method using the Bernstein polynomial and a finite Gaussian mixture model to construct a semi-parametric density estimator, which improves the approximation at the edges. Some asymptotic properties of the proposed approach are investigated, such as its probability convergence and its asymptotic normality. In order to evaluate the performance of the proposed estimator, a simulation study and some real data sets were carried out. Full article
(This article belongs to the Special Issue Statistical Learning of Networks and Functional Data)
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