Unlocking the Power of Probability and Statistics for Symmetry

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

Deadline for manuscript submissions: 1 December 2025 | Viewed by 1400

Special Issue Editors


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Guest Editor
Department of Mathematics and Center of Mathematics, University of Beira Interior, 6200 Covilhã, Portugal
Interests: applied statistics; data science; probability theory; statistical inference; statistical modeling
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Statistics, Federal University of Bahia, Salvador 40170-110, Brazil
Interests: statistical learning; time series forecasting; robust statistics; data science; applied statistics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In today’s highly data-driven world, the power of probability and statistics is becoming increasingly important in many areas of research. Symmetry has a strong affinity to probabilistic methods, as the study of symmetry allows us to identify and quantify patterns in data, and to make generalizations about the underlying mechanisms of nature.

This Special Issue seeks out innovative contributions from various disciplines related to probabilistic methods and their applications to the problems of symmetry. We welcome studies on theoretical developments and practical applications, as well as papers across a variety of topics, including, but not limited to, the following: artificial intelligence, atmospheric science, Bayesian statistics, bioinformatics, climate science, cognitive neuroscience, engineering, epidemiology, machine learning, mathematical physics, multivariate analysis, nonparametric statistics, operations research, probabilistic graphical models, quantitative finance, social sciences, statistical computing and simulation, statistical inference, stochastic modeling, survival analysis, and time series analysis.

We look forward to your submissions and to fostering further progress and understanding of the power of probability and statistics for symmetry.

Dr. Dário Ferreira
Prof. Dr. Paulo Canas Rodrigues
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

  • artificial intelligence
  • bioinformatics
  • machine learning
  • multivariate analysis
  • probability
  • statistics
  • stochastic modeling
  • symmetry

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Published Papers (1 paper)

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Research

34 pages, 31211 KB  
Article
Statistical Evaluation of Alpha-Powering Exponential Generalized Progressive Hybrid Censoring and Its Modeling for Medical and Engineering Sciences with Optimization Plans
by Heba S. Mohammed, Osama E. Abo-Kasem and Ahmed Elshahhat
Symmetry 2025, 17(9), 1473; https://doi.org/10.3390/sym17091473 - 6 Sep 2025
Viewed by 371
Abstract
This study explores advanced methods for analyzing the two-parameter alpha-power exponential (APE) distribution using data from a novel generalized progressive hybrid censoring scheme. The APE model is inherently asymmetric, exhibiting positive skewness across all valid parameter values due to its right-skewed exponential base, [...] Read more.
This study explores advanced methods for analyzing the two-parameter alpha-power exponential (APE) distribution using data from a novel generalized progressive hybrid censoring scheme. The APE model is inherently asymmetric, exhibiting positive skewness across all valid parameter values due to its right-skewed exponential base, with the alpha-power transformation amplifying or dampening this skewness depending on the power parameter. The proposed censoring design offers new insights into modeling lifetime data that exhibit non-monotonic hazard behaviors. It enhances testing efficiency by simultaneously imposing fixed-time constraints and ensuring a minimum number of failures, thereby improving inference quality over traditional censoring methods. We derive maximum likelihood and Bayesian estimates for the APE distribution parameters and key reliability measures, such as the reliability and hazard rate functions. Bayesian analysis is performed using independent gamma priors under a symmetric squared error loss, implemented via the Metropolis–Hastings algorithm. Interval estimation is addressed using two normality-based asymptotic confidence intervals and two credible intervals obtained through a simulated Markov Chain Monte Carlo procedure. Monte Carlo simulations across various censoring scenarios demonstrate the stable and superior precision of the proposed methods. Optimal censoring patterns are identified based on the observed Fisher information and its inverse. Two real-world case studies—breast cancer remission times and global oil reserve data—illustrate the practical utility of the APE model within the proposed censoring framework. These applications underscore the model’s capability to effectively analyze diverse reliability phenomena, bridging theoretical innovation with empirical relevance in lifetime data analysis. Full article
(This article belongs to the Special Issue Unlocking the Power of Probability and Statistics for Symmetry)
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