Advances in Robust and Nonparametric Statistical Techniques for Data Science

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D1: Probability and Statistics".

Deadline for manuscript submissions: 10 October 2025 | Viewed by 61

Special Issue Editor


E-Mail Website
Guest Editor
Department of Mathematics, University of Cauca, Popayan 500001, Colombia
Interests: nonparametric statistics; robust statistics; stochastic orders; survival and reliability

Special Issue Information

Dear Colleagues,

Nonparametric statistical techniques are a set of strategies useful for data analysis in cases where the assumptions necessary to apply parametric methods are not met. These nonparametric methods help to model and analyze practical situations with high-dimensional data in different disciplines with greater versatility while maintaining rigor. Therefore, currently they have become the dominant strategies used in data science.

This Special Issue of Mathematics, ‘Advances in Robust and Nonparametric Statistical Techniques for Data Science’, proposes to collect the recent contributions in theoretical and practical aspects of topics as the following:

  • Goodness-of-fit tests;
  • Nonparametric confidence intervals;
  • Density estimation;
  • Nonparametric regression;
  • Robust statistical methods;
  • Simulation and generation of synthetic data;
  • Statistical methods based on ranks;
  • Supervised and unsupervised classification;
  • Survival and reliability analysis;
  • Robust methods for risk estimation and portfolio selection;
  • Functional data;
  • Identification of outlier data.

Prof. Dr. Henry Laniado
Guest Editor

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Keywords

  • nonparametric statistics
  • robust statistics
  • data analytics
  • violation of statistical assumptions
  • data science

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Published Papers

This special issue is now open for submission.
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