Nonparametric and Semiparametric Approaches in Statistical Inference and Data Science: Theory, Methods and Applications
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D1: Probability and Statistics".
Deadline for manuscript submissions: 28 February 2026 | Viewed by 1070
Special Issue Editor
Interests: multivariate analysis; nonparametric statistics; permutation tests; composite indicators
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
You are kindly invited to contribute to this Special Issue, entitled “Nonparametric Statistical Methods and their Applications”, with an original research paper or a comprehensive review. The focus of this Special Issue is primarily on novel theoretical proposals, applications and/or the computational aspects of nonparametric statistical methods. The papers submitted should address a wide spectrum of topics concerning nonparametric and semiparametric approaches to inferential or exploratory data analysis. The scope of this Special Issue includes, but is not limited to, the following methodological topics:
- Rank tests
- Permutation tests
- Goodness-of-fit tests
- Bootstrap methods
- Nonparametric curve and/or density estimation
- Regression smoothing
- Symmetry testing
- Robust estimation
- Nonparametric filtering
- Ranked set sampling
- Bayesian non-parametrics
- Semiparametric models and procedures
Many statistical methods are based on assumptions that cannot be tested, are not plausible, or are not justified by asymptotic theories (e.g., they have small sample sizes). For these reasons, nonparametric statistical methods have become increasingly important and widespread in several empirical empirical disciplines. In this Special Issue, these methods may be applied in psychology, business and economics, finance, economic and social sciences, biomedical sciences, engineering, chemistry, environmental sciences, quality evaluation, multicriteria decision making, Big Data, DOE, computer science, computational intelligence and machine learning, among others.
Dr. Stefano Bonnini
Guest Editor
Manuscript Submission Information
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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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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
- nonparametric statistics
- semiparametric statistical methods
- curve estimation
- multivariate analysis
- statistical algorithms and machine learning
- resampling methods
- distribution-free techniques
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