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Statistical Machine Learning: Models and Its Applications
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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: closed (31 January 2026) | Viewed by 4063

Special Issue Editor

Dr. Charlotte Wang

E-Mail Website
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

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 250 words) can be sent to the Editorial Office for assessment.

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

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

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

17 pages, 834 KB  
Article
Improved Data-Driven Shrinkage Estimators for Regression Models Under Severe Multicollinearity
by Ali Rashash R. Alzahrani and Asma Ahmad Alzahrani
Mathematics 2026, 14(8), 1245; https://doi.org/10.3390/math14081245 - 9 Apr 2026
Viewed by 228
Abstract
Multicollinearity is a critical issue in regression analysis, often resulting in inflated variances and unstable parameter estimates. Ridge regression is a widely adopted solution to address this challenge; however, existing ridge estimators are typically tailored to specific scenarios, limiting their universal applicability. Akhtar [...] Read more.
Multicollinearity is a critical issue in regression analysis, often resulting in inflated variances and unstable parameter estimates. Ridge regression is a widely adopted solution to address this challenge; however, existing ridge estimators are typically tailored to specific scenarios, limiting their universal applicability. Akhtar and Alharthi developed ridge estimators based on condition-adjusted ridge estimators (CAREs) to handle severe multicollinearity issues. However, their approach did not account for the error variances in the estimation process. In this study, we propose improvements to these CAREs by incorporating error variances, resulting in the development of multiscale ridge estimators (MSRE1, MSRE2, MSRE3 and MSRE4) that more effectively address the challenges posed by severe multicollinearity. We compare the performance of our newly proposed estimators with ordinary least square (OLS) and other existing ridge estimators using both simulation studies and real-life datasets. The evaluation, based on estimated mean squared error (MSE), demonstrates that the proposed estimators consistently outperform existing methods, particularly in scenarios with significant multicollinearity, larger sample sizes, and higher predictor dimensions. Results from three real-life datasets further validate the proposed estimators’ ability to reduce estimation error and improve predictive accuracy across diverse practical applications. Full article
(This article belongs to the Special Issue Statistical Machine Learning: Models and Its Applications)
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37 pages, 1897 KB  
Article
A Bayesian Feature Weighting Model with Simplex-Constrained Dirichlet and Contamination-Aware Priors for Noisy Medical Data
by Mehmet Ali Cengiz, Zeynep Öztürk and Abdulmohsen Alharthi
Mathematics 2026, 14(8), 1243; https://doi.org/10.3390/math14081243 - 8 Apr 2026
Viewed by 358
Abstract
Feature weighting plays a central role in medical classification by enhancing predictive accuracy, interpretability, and clinical trust through the explicit quantification of variable relevance. Despite their widespread use, existing filter-, wrapper-, and embedded-based feature weighting methods are predominantly deterministic and exhibit pronounced sensitivity [...] Read more.
Feature weighting plays a central role in medical classification by enhancing predictive accuracy, interpretability, and clinical trust through the explicit quantification of variable relevance. Despite their widespread use, existing filter-, wrapper-, and embedded-based feature weighting methods are predominantly deterministic and exhibit pronounced sensitivity to label noise and outliers, which are pervasive in real-world medical data. This often results in unstable importance estimates and unreliable clinical interpretations. In this work, we introduce a novel Bayesian feature weighting model that fundamentally departs from existing approaches by jointly integrating simplex-constrained Dirichlet priors for global feature weights, hierarchical shrinkage priors for coefficient regularization, and contamination-aware priors for explicit modeling of label noise within a single coherent probabilistic framework. Unlike conventional Bayesian feature selection or robust classification models, the proposed formulation yields globally interpretable feature weights defined on the probability simplex, while simultaneously providing full posterior uncertainty quantification and robustness to both mislabeled observations and aberrant feature values through principled influence control. Comprehensive simulation studies across diverse contamination scenarios, together with applications to multiple real-world medical datasets, demonstrate that the proposed model consistently outperforms classical and state-of-the-art baselines in terms of discrimination, probabilistic calibration, and stability of feature-importance estimates. These results highlight the practical and methodological significance of the proposed framework as a robust, uncertainty-aware, and interpretable solution for medical decision making under noisy data conditions. Full article
(This article belongs to the Special Issue Statistical Machine Learning: Models and Its Applications)
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16 pages, 2791 KB  
Article
Adaptive Penalized Regression for High-Efficiency Estimation in Correlated Predictor Settings: A Data-Driven Shrinkage Approach
by Muhammad Shakir Khan and Amirah Saeed Alharthi
Mathematics 2025, 13(17), 2884; https://doi.org/10.3390/math13172884 - 6 Sep 2025
Cited by 4 | Viewed by 1119
Abstract
Penalized regression estimators have become widely adopted alternatives to ordinary least squares while analyzing collinear data, despite introducing some bias. However, existing penalized methods lack universal superiority across diverse data conditions. To address this limitation, we propose a novel adaptive ridge estimator that [...] Read more.
Penalized regression estimators have become widely adopted alternatives to ordinary least squares while analyzing collinear data, despite introducing some bias. However, existing penalized methods lack universal superiority across diverse data conditions. To address this limitation, we propose a novel adaptive ridge estimator that automatically adjusts its penalty structure based on key data characteristics: (1) the degree of predictor collinearity, (2) error variance, and (3) model dimensionality. Through comprehensive Monte Carlo simulations and real-world applications, we evaluate the estimator’s performance using mean squared error (MSE) as our primary criterion. Our results demonstrate that the proposed method consistently outperforms existing approaches across all considered scenarios, with particularly strong performance in challenging high-collinearity settings. The real-data applications further confirm the estimator’s practical utility and robustness. Full article
(This article belongs to the Special Issue Statistical Machine Learning: Models and Its Applications)
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25 pages, 563 KB  
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 1768
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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