A Comprehensive Review of Machine Learning Applications in Statistical Theory

Authors

  • James Onyango PhD Candidate, University of Nairobi, Kenya

DOI:

https://doi.org/10.61424/gjme.v1i1.147

Keywords:

Machine learning, Statistical theory, Bayesian statistics, Estimation accuracy, Predictive modeling

Abstract

The integration of machine learning techniques into statistical theory has propelled significant advancements in both fields, leading to innovative solutions in data analysis and decision-making processes. This comprehensive review examines the intersection of machine learning and statistical theory, highlighting the transformative impact of machine learning applications within statistical frameworks. The study systematically categorizes and evaluates a wide array of machine learning methodologies, including supervised, unsupervised, semisupervised, and reinforcement learning, that have been adapted to address complex statistical challenges. Key areas of application discussed include hypothesis testing, predictive modeling, Bayesian statistics, and high-dimensional data analysis. The review further explores how machine learning enhances statistical inference, improves estimation accuracy, and expedites computational efficiency. Additionally, the paper identifies ongoing challenges such as model interpretability, overfitting, and the need for robust validation frameworks. By consolidating insights from numerous studies, this review provides a foundational understanding of the symbiotic relationship between machine learning and statistical theory, offering valuable perspectives for researchers and practitioners aiming to leverage machine learning for advanced statistical analysis. Future directions for research are proposed, emphasizing the importance of interdisciplinary collaboration to address the evolving complexities of data-driven environments.

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Published

2024-11-21 — Updated on 2024-11-23

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How to Cite

Onyango, J. (2024). A Comprehensive Review of Machine Learning Applications in Statistical Theory. Global Journal of Mathematics and Statistics, 1(1), 46–55. https://doi.org/10.61424/gjme.v1i1.147 (Original work published November 21, 2024)