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Symmetry
Special Issues
Symmetric or Asymmetric Distributions and Its Applications
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Symmetric or Asymmetric Distributions and Its Applications

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

Deadline for manuscript submissions: 30 December 2024 | Viewed by 5532

Special Issue Editor

Dr. Xuelong Hu

E-Mail Website
Guest Editor
School of Management, Nanjing University of Posts and Telecommunications, Nanjing 210049, China
Interests: statistical process control; statistical transfer learning

Special Issue Information

Dear Colleagues,

Among the wide applications of probability distributions and statistical models in different areas such as economics, management, engineering, biomedicine, healthcare, and so on, the implementation of symmetric or asymmetric distributions in process monitoring are of high importance in recent years and we can see considerably increasing studies in these areas.

Authors are encouraged to submit theorical or applied works in different related fields. To name a few, we can mention (i) statistical process monitoring, (ii) control charts, (iii) applied statistics, (iv) modeling of financial process, and so on. Researchers are invited to contribute original works, review articles, and case studies related to symmetric or asymmetric distributions. In particular, this Special Issue intends to study methodologies for the existing discrete and continuous and the univariate and multivariate distributions in consideration of symmetric and asymmetric distributions.

Dr. Xuelong Hu
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. 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 2400 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

  • symmetric or asymmetric distributions
  • univariate distributions
  • multivariate distributions

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Published Papers (5 papers)

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Research

20 pages, 1855 KiB  
Article
A Hybrid Data Envelopment Analysis–Random Forest Methodology for Evaluating Green Innovation Efficiency in an Asymmetric Environment
by Limei Chen, Xiaohan Xie, Yao Yao, Weidong Huang and Gongzhi Luo
Symmetry 2024, 16(8), 960; https://doi.org/10.3390/sym16080960 - 28 Jul 2024
Viewed by 789
Abstract
The accurate evaluation of green innovation efficiency is a critical prerequisite for enterprises to achieve sustainable development goals and improve environmental performance and economic efficiency. This paper evaluates the green innovation efficiency of 72 new-energy enterprises by using a hybrid method of Data [...] Read more.
The accurate evaluation of green innovation efficiency is a critical prerequisite for enterprises to achieve sustainable development goals and improve environmental performance and economic efficiency. This paper evaluates the green innovation efficiency of 72 new-energy enterprises by using a hybrid method of Data Envelopment Analysis (DEA) and a random forest model. The non-parametric DEA model is combined with the parametric SFA model to analyze the real green innovation efficiency on the basis of removing environmental factors and random factors. Then, the random forest model based on a nonlinear relationship is used to evaluate factors impacting green innovation efficiency. This paper proposes a comprehensive evaluation method designed to assess the green innovation efficiency of new-energy enterprises. By applying this method, companies can gain a comprehensive understanding of the current performance in green innovation, facilitating informed decision-making and accelerating sustainable development. Full article
(This article belongs to the Special Issue Symmetric or Asymmetric Distributions and Its Applications)
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19 pages, 1853 KiB  
Article
A Symmetrical Analysis of Decision Making: Introducing the Gaussian Negative Binomial Mixture with a Latent Class Choice Model
by Irsa Sajjad, Ibrahim Ali Nafisah, Mohammed M. A. Almazah, Osama Abdulaziz Alamri and Javid Gani Dar
Symmetry 2024, 16(7), 908; https://doi.org/10.3390/sym16070908 - 16 Jul 2024
Cited by 1 | Viewed by 749
Abstract
This research presents a model called the ‘Gaussian negative binomial mixture with a latent class choice model’, which serves as a robust and efficient tool for analyzing decisions across different areas. Our innovative model combines elements of mixture models, negative binomial distributions, and [...] Read more.
This research presents a model called the ‘Gaussian negative binomial mixture with a latent class choice model’, which serves as a robust and efficient tool for analyzing decisions across different areas. Our innovative model combines elements of mixture models, negative binomial distributions, and latent class choice modeling to create an approach that captures the complexities of decision-making processes. We explain how the model is formulated and estimated, showcasing its effectiveness in analyzing and predicting choices in scenarios. Through the use of a dataset, we demonstrate the performance of this method, marking a significant advancement in choice modeling. Our results highlight the applications of this model and point towards promising directions for future research, especially in exploring symmetrical patterns and structures, within decision-making processes. Full article
(This article belongs to the Special Issue Symmetric or Asymmetric Distributions and Its Applications)
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26 pages, 1418 KiB  
Article
A Novel Three-Parameter Nadarajah Haghighi Model: Entropy Measures, Inference, and Applications
by Etaf Alshawarbeh, Fatimah M. Alghamdi, Mohammed Amine Meraou, Hassan M. Aljohani, Mahmoud Abdelraouf, Fathy H. Riad, Sara Mohamed Ahmed Alsheikh and Meshayil M. Alsolmi
Symmetry 2024, 16(6), 751; https://doi.org/10.3390/sym16060751 - 16 Jun 2024
Cited by 1 | Viewed by 779
Abstract
The fitting and modeling of skewed, complex, symmetric, and asymmetric datasets is an exciting research topic in many fields of applied sciences: notably, lifetime, medical, and financial sciences. This paper introduces a heavy-tailed Nadarajah Haghighi model by compounding the heavy-tailed family and Nadarajah [...] Read more.
The fitting and modeling of skewed, complex, symmetric, and asymmetric datasets is an exciting research topic in many fields of applied sciences: notably, lifetime, medical, and financial sciences. This paper introduces a heavy-tailed Nadarajah Haghighi model by compounding the heavy-tailed family and Nadarajah Haghighi distribution. The model obtained has three parameters that account for the scale and shape of the distribution. The proposed distribution’s fundamental characteristics, such as the probability density, cumulative distribution, hazard rate, and survival functions, are provided, several key statistical properties are established, and several entropy information measures are proposed. Estimation of model parameters is performed via a maximum likelihood estimator procedure. Further, different simulation experiments are conducted to demonstrate the proposed estimator’s performance using measures like the average estimate, the average bias, and the associated mean square error. Finally, we apply our proposed model to analyze three different real datasets. In our illustration, we compare the practicality of the recommended model with several well-known competing models. Full article
(This article belongs to the Special Issue Symmetric or Asymmetric Distributions and Its Applications)
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24 pages, 1060 KiB  
Article
A New Three-Parameter Inverse Rayleigh Distribution: Simulation and Application to Real Data
by Muzafer Shala and Faton Merovci
Symmetry 2024, 16(5), 634; https://doi.org/10.3390/sym16050634 - 20 May 2024
Viewed by 1295
Abstract
In this paper, we introduce a new three-parameter inverse Rayleigh distribution that extends the inverse Rayleigh distribution, constructed based on the generalized transmuted family of distributions proposed by Alizadeh, Merovci, and Hamedani. We explore statistical properties such as the quantile function, moments, harmonic [...] Read more.
In this paper, we introduce a new three-parameter inverse Rayleigh distribution that extends the inverse Rayleigh distribution, constructed based on the generalized transmuted family of distributions proposed by Alizadeh, Merovci, and Hamedani. We explore statistical properties such as the quantile function, moments, harmonic mean, mean deviation, stress–strength reliability, and entropy. Parameter estimation is performed using various methods, including maximum likelihood, least squares, the method of the maximum product of spacings, and the method of Cramér–von Mises. The usefulness of the new three-parameter inverse Rayleigh distribution is illustrated by modeling a real dataset, demonstrating its superior fit compared to several other distributions. Full article
(This article belongs to the Special Issue Symmetric or Asymmetric Distributions and Its Applications)
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18 pages, 937 KiB  
Article
An Improved Slack Based Measure Model for Evaluating Green Innovation Efficiency Based on Asymmetric Data
by Limei Chen, Xiaohan Xie and Siyun Tao
Symmetry 2024, 16(4), 429; https://doi.org/10.3390/sym16040429 - 4 Apr 2024
Viewed by 1192
Abstract
Nowadays, one of the main challenges facing green innovation management is how to enhance the performance of innovation processes by utilizing asymmetric input and output data. Therefore, this paper develops an improved SBM model analysis framework for evaluating the green innovation efficiency of [...] Read more.
Nowadays, one of the main challenges facing green innovation management is how to enhance the performance of innovation processes by utilizing asymmetric input and output data. Therefore, this paper develops an improved SBM model analysis framework for evaluating the green innovation efficiency of asymmetric input and output data. The framework is applied to assess the technical (TE), managerial (PTE), and scale (SE) efficiencies of new energy companies under three input variables (R&D personnel input, R&D capital input, and comprehensive energy consumption input), two desirable output variables (green technology output and economic output), and one undesirable output variable (greenhouse gas emissions). Then, environmental factors and random factors are eliminated from the obtained input slack variables based on the SFA model, placing decision-making units in a homogeneous environment. The results demonstrate that TE, PTE, and SE are improved after eliminating environmental factors and random factors. Subsequently, based on the entropy method, this paper classifies companies’ green innovation patterns into four categories and provides targeted solutions. The purpose of this paper is to provide an evaluation method for new energy companies to understand green innovation efficiency and assist decision makers in identifying the most optimal resource allocation approach. The proposed improved SBM model contributes to the literature and to industry practice by (1) providing a reliable evaluation of green innovation efficiency under asymmetric input and output data; (2) determining effective improvement actions based on a slack analysis of environmental variables and random variables that lead to improved process performance; and (3) making fuzzy innovation performance efficient to facilitate understanding and managing innovation resource allocation quality. Full article
(This article belongs to the Special Issue Symmetric or Asymmetric Distributions and Its Applications)
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