Special Issue "Symmetric and Asymmetric Distributions: Theoretical Developments and Applications"

A special issue of Symmetry (ISSN 2073-8994).

Deadline for manuscript submissions: 30 September 2019.

Special Issue Editor

Guest Editor
Prof. Dr. Emilio Gómez-Déniz

Department of Quantitative Methods in Economics and Business Administration. University of Las Palmas de Gran Canaria, Spain
Website | E-Mail
Interests: Distributions Theory; Bayesian Statistics; Robustness; Bayesian Applications in Economics (Actuarial, Credibility, Ruin Theory)

Special Issue Information

Dear Colleagues,

In recent decades, the number of theoretical and applied statistical works related to symmetric distributions has proliferated; the majority of these works are based on the Azzalini's scheme. This strategy uses a symmetric distribution as baseline, then a parameter is added to the standard model that controls the asymmetry of the new family of probability distributions. The most widespread and popular model is the one based on the normal distribution that gives rise to the skew normal distribution.

In this Special Issue on symmetrical distributions, researchers are invited to contribute original research papers and case studies related to this topic. Submissions that extend the Azzalini's scheme or propose alternatives, both for univariate and multivariate, continuous and discrete distributions are welcome. Researchers are also invited to offer theoretical and applied proposals that have connection with this subject. Applications in the fields of economics (inflation forecasting, stochastic frontier models, insurance, etc.), environmental sciences (climate changes, for instance), biometrics, engineering (reliability, classification of satellite image, etc.), and others fields are welcome.

This Special Issue is open to both original research articles and review articles, both theoretical and applied, within the area of symmetric and asymmetric distributions.

Prof. Dr. Emilio Gómez-Déniz
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 1400 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

  • Applications
  • Bayesian
  • Kurtosis
  • Order Statistics
  • Simulation
  • Skewness

Published Papers (3 papers)

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Research

Open AccessArticle
An Asymmetric Bimodal Distribution with Application to Quantile Regression
Symmetry 2019, 11(7), 899; https://doi.org/10.3390/sym11070899
Received: 27 May 2019 / Revised: 25 June 2019 / Accepted: 25 June 2019 / Published: 10 July 2019
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Abstract
In this article, we study an extension of the sinh Cauchy model in order to obtain asymmetric bimodality. The behavior of the distribution may be either unimodal or bimodal. We calculate its cumulative distribution function and use it to carry out quantile regression. [...] Read more.
In this article, we study an extension of the sinh Cauchy model in order to obtain asymmetric bimodality. The behavior of the distribution may be either unimodal or bimodal. We calculate its cumulative distribution function and use it to carry out quantile regression. We calculate the maximum likelihood estimators and carry out a simulation study. Two applications are analyzed based on real data to illustrate the flexibility of the distribution for modeling unimodal and bimodal data. Full article
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Open AccessArticle
Univariate and Bivariate Models Related to the Generalized Epsilon–Skew–Cauchy Distribution
Symmetry 2019, 11(6), 794; https://doi.org/10.3390/sym11060794
Received: 8 May 2019 / Revised: 27 May 2019 / Accepted: 10 June 2019 / Published: 14 June 2019
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Abstract
In this paper, we consider a stochastic representation of the epsilon–skew–Cauchy distribution, viewed as a member of the family of skewed distributions discussed in Arellano-Valle et al. (2005). The stochastic representation facilitates derivation of distributional properties of the model. In addition, we introduce [...] Read more.
In this paper, we consider a stochastic representation of the epsilon–skew–Cauchy distribution, viewed as a member of the family of skewed distributions discussed in Arellano-Valle et al. (2005). The stochastic representation facilitates derivation of distributional properties of the model. In addition, we introduce symmetric and asymmetric extensions of the Cauchy distribution, together with an extension of the epsilon–skew–Cauchy distribution. Multivariate versions of these distributions can be envisioned. Bivariate examples are discussed in some detail. Full article
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Open AccessArticle
A Note on Ordering Probability Distributions by Skewness
Symmetry 2018, 10(7), 286; https://doi.org/10.3390/sym10070286
Received: 15 June 2018 / Revised: 9 July 2018 / Accepted: 10 July 2018 / Published: 16 July 2018
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Abstract
This paper describes a complementary tool for fitting probabilistic distributions in data analysis. First, we examine the well known bivariate index of skewness and the aggregate skewness function, and then introduce orderings of the skewness of probability distributions. Using an example, we highlight [...] Read more.
This paper describes a complementary tool for fitting probabilistic distributions in data analysis. First, we examine the well known bivariate index of skewness and the aggregate skewness function, and then introduce orderings of the skewness of probability distributions. Using an example, we highlight the advantages of this approach and then present results for these orderings in common uniparametric families of continuous distributions, showing that the orderings are well suited to the intuitive conception of skewness and, moreover, that the skewness can be controlled via the parameter values. Full article
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