Symmetry/Asymmetry in Goodness-of-Fit Testing and Statistical Inference Using Non-Parametric Approaches
A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Mathematics".
Deadline for manuscript submissions: 31 October 2026 | Viewed by 244
Special Issue Editors
Interests: empirical likelihood method; goodness-of-fit testing; non-parametric statistics; survival analysis
Interests: survival analysis; empirical likelihood method and its application; nonparametric statistics; bioinformatics; ROC curve analysis; Monte Carlo methods; statistical modeling of fuzzy systems
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Special Issue Information
Dear Colleagues,
Non-parametric methods provide powerful tools for statistical inference, particularly when classical assumptions are violated. Of late, the use of non-parametric techniques such as the empirical likelihood (EL) methodology has led to the development of goodness-of-fit (GoF) tests for various symmetric and asymetric distributions that are superior under several alternatives. Advancing theoretical methodology is therefore crucial for further investigating the strengths and challenges of non-parametric methods in GoF testing. For instance, EL moment-based normality tests may lack power against some symmetric alternatives.
Several parametric estimation methods require data to be consistent with normality, which is symmetric in nature. This assumption is frequently violated in practice, where data may exhibit skewness and kurtosis indicative of underlying asymmetry. Non-parametric methods offer a robust alternative for estimation and inference under these conditions. In biostatistics, issues of symmetry may also arise in the form of censored observations. Specifically, in survival analysis, non-parametric estimators such as the Kaplan–Meier and Turnbull methods address censored data, where the fuzziness in interval-censoring often requires centrality-based imputation methods.
We invite research on how symmetry and asymmetry guide the development of GoF tests, robust estimators, and inference procedures, advancing non-parametric methods in modern statistics. Applications related to biostatistics, epidemiology, and health sciences are encouraged.
In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:
- Symmetry and asymmetry in goodness-of-fit testing;
- Robust and non-parametric methods in statistical inference;
- Empirical likelihood methods and extensions in goodness-of-fit testing;
- Non-parametric estimation and survival analysis with censored data;
- Simulation-based approaches in non-parametric inference;
- Optimization algorithms for non-parametric inference;
- Applications of non-parametric methods in biostatistics.
We look forward to receiving your contributions.
Dr. Chioneso Show Marange
Prof. Dr. Yichuan Zhao
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
- goodness-of-fit testing
- non-parametric statistics
- empirical likelihood
- survival analysis
- censored data
- fuzzy
- test for symmetry
- optimization algorithms
- Monte Carlo simulations
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