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 June 2024 | Viewed by 909

Special Issue Editor


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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

Published Papers (1 paper)

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Research

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 515
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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