Statistical Machine Learning: Models and Its Applications

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

Deadline for manuscript submissions: 31 January 2026 | Viewed by 359

Special Issue Editor


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Guest Editor
Master of Public Health Program & Institute of Health Data Analytics and Statistics, College of Public Health, National Taiwan University, Taipei, Taiwan
Interests: statistical and machine learning; Bayesian statistics; wearable device data analysis

Special Issue Information

Dear Colleagues,

I invite you to submit your latest research to this Special Issue titled "Statistical Machine Learning: Models and Its Applications". This Special Issue highlights the latest advancements, theoretical foundations, and innovative applications of statistical machine learning (SML) in diverse fields. In the era of data explosion, various application domains strive to uncover hidden insights within data and leverage machine learning, artificial intelligence (AI), and statistical methods to address various clinical and practical challenges. As machine learning techniques continue to evolve, statistical methods play a pivotal role in ensuring robust, interpretable, and efficient models. This Special Issue aims to bring together researchers, practitioners, and experts to explore the intersection of statistical methods and machine learning algorithms, fostering the development of new insights and practical solutions.

Topics of Interest

We welcome submissions on topics including, but not limited to, the following:

  • The development of novel statistical machine learning models and algorithms.
  • Statistical learning in high-dimensional data and big data environments.
  • Applications of statistical machine learning in finance, healthcare, bioinformatics, social sciences, and other domains.
  • Methods or applications for data mining and text mining.
  • Interpretability and fairness in machine learning models through statistical techniques.

Dr. Charlotte Wang
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.

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Keywords

  • machine learning
  • statistical learning
  • data mining
  • data science
  • data visualization
  • feature engineering
  • generative AI
  • statistical modeling

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Published Papers (1 paper)

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Research

25 pages, 563 KiB  
Article
A Meta-Learning Approach for Estimating Heterogeneous Treatment Effects Under Hölder Continuity
by Zhihao Zhao and Congyang Zhou
Mathematics 2025, 13(11), 1739; https://doi.org/10.3390/math13111739 - 24 May 2025
Viewed by 231
Abstract
Estimating heterogeneous treatment effects plays a vital role in many statistical applications, such as precision medicine and precision marketing. In this paper, we propose a novel meta-learner, termed RXlearner for estimating the conditional average treatment effect (CATE) within the general framework of meta-algorithms. [...] Read more.
Estimating heterogeneous treatment effects plays a vital role in many statistical applications, such as precision medicine and precision marketing. In this paper, we propose a novel meta-learner, termed RXlearner for estimating the conditional average treatment effect (CATE) within the general framework of meta-algorithms. RXlearner enhances the weighting mechanism of the traditional Xlearner to improve estimation accuracy. We establish non-asymptotic error bounds for RXlearner under a continuity classification criterion, specifically assuming that the response function satisfies Hölder continuity. Moreover, we show that these bounds are achievable by selecting an appropriate base learner. The effectiveness of the proposed method is validated through extensive simulation studies and a real-world data experiment. Full article
(This article belongs to the Special Issue Statistical Machine Learning: Models and Its Applications)
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