Special Issue "Symmetry in Statistics and Data Science"

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Mathematics and Symmetry".

Deadline for manuscript submissions: 31 August 2022.

Special Issue Editor

Dr. Christophe Chesneau
E-Mail Website
Guest Editor
Department of Mathematics, Université de Caen, LMNO, Campus II, Science 3, 14032 Caen, France
Interests: mathematical statistics; applied statistics; data analysis; probability; applied probability; analytic inequalities
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Symmetry is a central notion in statistics and data science, appearing in various forms in artificial intelligence, data analysis, distribution theory, modeling, networks, nonparametric estimation, parametric estimation, high dimensional data, statistical tests, as well as in many other branches of modern interest.
The objective of this Special Issue is to publish highly motivated, original, and innovative research articles that use the notion of symmetry on current topics in statistics and data science.
The scope includes but is not limited to the following topics:

  • Artificial intelligence;
  • Bayes methods;  
  • Data analysis;
  • Dimension reduction and variable selection;
  • Distribution theory;
  • Econometrics;
  • Estimation;
  • Inference with high-dimensional data;
  • Inference of stochastic processes;
  • Machine learning;
  • Modelling;
  • Nonparametric function estimation;
  • Sample surveys;
  • Statistical algorithms;
  • Statistical methods for imaging;
  • Time series analysis.

Dr. Christophe Chesneau
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 papers will be 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. Symmetry 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 1800 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.

Published Papers (2 papers)

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Research

Article
Bayesian Reference Analysis for the Generalized Normal Linear Regression Model
Symmetry 2021, 13(5), 856; https://doi.org/10.3390/sym13050856 - 12 May 2021
Viewed by 426
Abstract
This article proposes the use of the Bayesian reference analysis to estimate the parameters of the generalized normal linear regression model. It is shown that the reference prior led to a proper posterior distribution, while the Jeffreys prior returned an improper one. The [...] Read more.
This article proposes the use of the Bayesian reference analysis to estimate the parameters of the generalized normal linear regression model. It is shown that the reference prior led to a proper posterior distribution, while the Jeffreys prior returned an improper one. The inferential purposes were obtained via Markov Chain Monte Carlo (MCMC). Furthermore, diagnostic techniques based on the Kullback–Leibler divergence were used. The proposed method was illustrated using artificial data and real data on the height and diameter of Eucalyptus clones from Brazil. Full article
(This article belongs to the Special Issue Symmetry in Statistics and Data Science)
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Article
Computing Expectiles Using k-Nearest Neighbours Approach
Symmetry 2021, 13(4), 645; https://doi.org/10.3390/sym13040645 - 11 Apr 2021
Viewed by 311
Abstract
Expectiles have gained considerable attention in recent years due to wide applications in many areas. In this study, the k-nearest neighbours approach, together with the asymmetric least squares loss function, called ex-kNN, is proposed for computing expectiles. Firstly, the effect of various distance measures on ex-kNN in terms of test error and computational time is evaluated. It is found that Canberra, Lorentzian, and Soergel distance measures lead to minimum test error, whereas Euclidean, Canberra, and Average of (L1,L) lead to a low computational cost. Secondly, the performance of ex-kNN is compared with existing packages er-boost and ex-svm for computing expectiles that are based on nine real life examples. Depending on the nature of data, the ex-kNN showed two to 10 times better performance than er-boost and comparable performance with ex-svm regarding test error. Computationally, the ex-kNN is found two to five times faster than ex-svm and much faster than er-boost, particularly, in the case of high dimensional data. Full article
(This article belongs to the Special Issue Symmetry in Statistics and Data Science)
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