Aims and Scope
The Scientific Journal of Mathematics and Statistics (SJMS) is a peer-reviewed journal dedicated to high-quality research that advances mathematics, statistics, and their interdisciplinary applications. The journal invites statisticians, mathematicians, researchers, academicians, data scientists and practitioners to share innovative theories, methodologies, computational techniques and practical solutions that address scientific, engineering, industrial, and societal challenges. Through the dissemination of rigorous and impactful research, SJMS contributes to advancements in mathematical and statistical sciences globally.
SJMS publishes original research articles, review articles, short communications, and methodological papers that advance scientific knowledge and technological innovation.
The Scientific Journal of Mathematics and Statistics is committed to publishing rigorous, peer-reviewed research that advances theoretical and practical applications of mathematics and statistics. The journal seeks to foster innovation, encourage interdisciplinary collaboration, and support the development of novel mathematical models, statistical methodologies, and computational approaches. By providing an accessible platform for researchers worldwide, the journal promotes the dissemination of reliable scientific knowledge while upholding the highest standards of research quality, integrity, transparency, and ethical publishing.
The Scientific Journal of Mathematics and Statistics welcomes high-impact research in mathematics, statistics, and related disciplines including (but not limited to):
- Theoretical and applied mathematics
- Statistical theory and methodology
- Algebra, geometry, mathematical analysis and differential equations
- Numerical analysis, optimization, operations research, mathematical modeling and scientific computing
- Probability, statistical inference, regression analysis, time series analysis, multivariate statistics, Bayesian statistics and statistical computing
- Computational mathematics, data science, machine learning and artificial intelligence
- Interdisciplinary applications of mathematics and statistics in engineering, medicine, economics, finance, environmental science and social sciences
- Innovative mathematical and statistical approaches that address real-world scientific and technological challenges