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

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

Deadline for manuscript submissions: 31 August 2020.

Special Issue Editors

Prof. Dr. Emilio Gómez Déniz
Website
Guest Editor
Department of Quantitative Methods and TIDES Institute, University of Las Palmas de Gran Canaria, Campus de Tafira s/n, 35017-Las Palmas de Gran Canaria, Spain
Interests: Distributions Theory; Bayesian Statistics; Robustness; Bayesian Applications in Economics (Actuarial, Credibility, Ruin Theory)
Special Issues and Collections in MDPI journals
Dr. Enrique Calderín-Ojeda

Guest Editor
Centre for Actuarial Studies, Department of Economics, The University of Melbourne, Australia

Special Issue Information

Dear Colleagues,

This Special Issue is the continuation of the previous one recently published in Symmetry with the same title. Among the wide range of probability distributions available in different scenarios, the property of symmetry (and nonsymmetry) represents an important characteristic when modeling and making predictions. Obviously, current computational advances facilitate calculations that include numerous special functions that were prohibited in practical applications in the past. Hence, the catalog of probabilistic families in the literature has considerably increased.

 

In this Special Issue on symmetric and asymmetric distributions, researchers are invited to contribute original works and case studies related to this topic. Theoretical and applied proposals that extend the Azzalini, Jones or Marshall and Olkin schemes, or simply valid alternatives will be welcome. Authors are also encouraged to submit applied works in the field of economics (ination forecast, income and wealth, stochastic frontier models, insurance, duration models, econophysics, etc.), environmental sciences (catastrophic events, climate changes, for example), biometrics, engineering (reliability, satellite image classification, etc.), and medicine (studies related to cancer disease, cure rate models, etc.), among other areas of applications. In particular, although not limited to this, this Special Issue is intended to offer alternative methodologies to the existing modeling techniques, and it is open to original research and review articles, both theoretical and applied (empirical data adjustment, regression, Bayesian study, etc.), within the area of symmetric and asymmetric, discrete and continuous, and univariate and multivariate distributions.

Prof. Dr. Emilio Gómez Déniz
Dr. Enrique Calderín-Ojeda
Guest Editors

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
  • regression
  • simulation
  • skewness

Published Papers (3 papers)

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Research

Open AccessArticle
A Gamma-Type Distribution with Applications
Symmetry 2020, 12(5), 870; https://doi.org/10.3390/sym12050870 - 25 May 2020
Abstract
This article introduces a new probability distribution capable of modeling positive data that present different levels of asymmetry and high levels of kurtosis. A slashed quasi-gamma random variable is defined as the quotient of independent random variables, a generalized gamma is the numerator, [...] Read more.
This article introduces a new probability distribution capable of modeling positive data that present different levels of asymmetry and high levels of kurtosis. A slashed quasi-gamma random variable is defined as the quotient of independent random variables, a generalized gamma is the numerator, and a power of a standard uniform variable is the denominator. The result is a new three-parameter distribution (scale, shape, and kurtosis) that does not present the identifiability problem presented by the generalized gamma distribution. Maximum likelihood (ML) estimation is implemented for parameter estimation. The results of two real data applications revealed a good performance in real settings. Full article
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Open AccessArticle
Bias Reduction for the Marshall-Olkin Extended Family of Distributions with Application to an Airplane’s Air Conditioning System and Precipitation Data
Symmetry 2020, 12(5), 851; https://doi.org/10.3390/sym12050851 - 22 May 2020
Abstract
The Marshall-Olkin extended family of distributions is an alternative for modeling lifetimes, and considers more or less asymmetry than its parent model, achieved by incorporating just one extra parameter. We investigate the bias of maximum likelihood estimators and use it to develop an [...] Read more.
The Marshall-Olkin extended family of distributions is an alternative for modeling lifetimes, and considers more or less asymmetry than its parent model, achieved by incorporating just one extra parameter. We investigate the bias of maximum likelihood estimators and use it to develop an estimator with less bias than traditional estimators, by a modification of the score function. Unlike other proposals, in this paper, we consider a bias reduction methodology that can be applied to any member of the family and not necessarily to any particular distribution. We conduct a Monte Carlo simulation in order to study the performance of the corrected estimators in finite samples. This simulation shows that the maximum likelihood estimator is quite biased and the proposed estimator is much less biased; in small sample sizes, the bias is reduced by around 50 percent. Two applications, related to the air conditioning system of an airplane and precipitations, are presented to illustrate the results. In those applications, the bias reduction for the shape parameters is close to 25% and the bias reduction also reduces, among others things, the width of the 95% confidence intervals for quantiles lower than 0.594. Full article
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Open AccessFeature PaperArticle
Generalising Exponential Distributions Using an Extended Marshall–Olkin Procedure
Symmetry 2020, 12(3), 464; https://doi.org/10.3390/sym12030464 - 15 Mar 2020
Abstract
This paper presents a three-parameter family of distributions which includes the common exponential and the Marshall–Olkin exponential as special cases. This distribution exhibits a monotone failure rate function, which makes it appealing for practitioners interested in reliability, and means it can be included [...] Read more.
This paper presents a three-parameter family of distributions which includes the common exponential and the Marshall–Olkin exponential as special cases. This distribution exhibits a monotone failure rate function, which makes it appealing for practitioners interested in reliability, and means it can be included in the catalogue of appropriate non-symmetric distributions to model these issues, such as the gamma and Weibull three-parameter families. Given the lack of symmetry of this kind of distribution, various statistical and reliability properties of this model are examined. Numerical examples based on real data reflect the suitable behaviour of this distribution for modelling purposes. Full article
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