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Symmetry
Special Issues
Symmetric Studies of Distributions in Statistical Models
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Symmetric Studies of Distributions in Statistical Models

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

Deadline for manuscript submissions: closed (31 May 2025) | Viewed by 348

Special Issue Editors

Dr. Xiao Xu

E-Mail Website
Guest Editor
College of Automation, Nanjing University of Posts and Telecommunications, Wenyuan Road 9, Nanjing 210023, China
Interests: power quality; renewable hosting capacity; smart grid; photovoltaic systems; photovoltaic inverters; energy
Prof. Dr. Hui Gao

E-Mail Website
Guest Editor
School of Modern Posts, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
Interests: electric vehicle charging process safety state monitoring and interaction with the power grid process state analysis
Prof. Dr. Jun Xie

E-Mail Website
Guest Editor
College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
Interests: safe and economical operation of modern power system; market-oriented operation mechanism of energy and electricity; renewable energy generation and integrated energy system

Special Issue Information

Dear Colleagues,

Symmetric distributions are commonly employed in parametric statistical modeling for various applications, such as reliability and robustness analysis, due to their strong convergence to population distribution. However, many experimental datasets, particularly in financial contexts and reliability studies, require models with heavier or lighter tails than standard parametric families or a model featuring a tail cutoff. In these instances, asymmetric distributions including Laplace, exponential power, and Weibull distributions have gained increased attention. Therefore, we welcome submissions that emphasize the novel applications of symmetric and asymmetric distributions to address data analysis challenges in complex engineering problems within real-world contexts.

Dr. Xiao Xu
Prof. Dr. Hui Gao
Prof. Dr. Jun Xie
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 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

  • statistical modeling
  • parametric model
  • symmetric distribution
  • asymmetric distribution
  • data analysis
  • real-world scenario

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Published Papers (1 paper)

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Research

23 pages, 1601 KiB  
Article
Level-Wise Feature-Guided Cascading Ensembles for Credit Scoring
by Yao Zou and Guanghua Cheng
Symmetry 2025, 17(6), 914; https://doi.org/10.3390/sym17060914 - 10 Jun 2025
Abstract
Accurate credit scoring models are essential for financial risk management, yet conventional approaches often fail to address the complexities of high-dimensional, heterogeneous credit data, particularly in capturing nonlinear relationships and hierarchical dependencies, ultimately compromising predictive performance. To overcome these limitations, this paper introduces [...] Read more.
Accurate credit scoring models are essential for financial risk management, yet conventional approaches often fail to address the complexities of high-dimensional, heterogeneous credit data, particularly in capturing nonlinear relationships and hierarchical dependencies, ultimately compromising predictive performance. To overcome these limitations, this paper introduces the level-wise feature-guided cascading ensemble (LFGCE) model, a novel framework that integrates hierarchical feature selection with cascading ensemble learning to systematically uncover latent feature hierarchies. The LFGCE framework leverages symmetry principles in its cascading architecture, where each ensemble layer maintains structural symmetry in processing its assigned feature subset while asymmetrically contributing to the final prediction through hierarchical information fusion. The LFGCE model operates through two synergistic mechanisms: (1) a hierarchical feature selection strategy that quantifies feature importance and partitions the feature space into progressively predictive subsets, thereby reducing dimensionality while preserving discriminative information, and (2) a cascading ensemble architecture where each layer specializes in learning risk patterns from its assigned feature subset, while iteratively incorporating outputs from preceding layers to enable cross-level information fusion. This dual process of hierarchical feature refinement and layered ensemble learning allows the LFGCE to extract deep, robust representations of credit risk. Empirical validation on four public credit datasets (Australian Credit, German Credit, Japan Credit, and Taiwan Credit) demonstrates that the LFGCE achieves an average AUC improvement of 0.23% over XGBoost (Python 3.13) and 0.63% over deep neural networks, confirming its superior predictive accuracy. Full article
(This article belongs to the Special Issue Symmetric Studies of Distributions in Statistical Models)
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