Aims and Scope
Mikailalsys Journal of Mathematics and Statistics publishes peer-reviewed scholarship that advances pure and applied mathematics, statistics, and data-analytic methods. The journal welcomes rigorous theoretical, methodological, and computational work that develops new mathematical results, statistical theory, and reproducible approaches to modeling, inference, and decision-making across scientific and applied domains.
Mathematics Statistics Modeling & Inference
Aims
Advance Mathematical and Statistical Theory
Publish high-quality research that strengthens mathematics and statistics through original theoretical results and rigorous methodological contributions.
Develop Robust Methods for Data and Decision-Making
Disseminate reproducible approaches to modeling, estimation, inference, prediction, optimization, and uncertainty quantification across applied domains.
Promote Reproducibility and Research Integrity
Encourage transparent reporting, open methods, and replicable computation to strengthen reliability and cumulative knowledge.
Scope
The journal considers original research articles, systematic reviews, and theoretically informed papers in mathematics and statistics, including computational and applied contributions. Submissions should demonstrate rigorous proofs or methodological justification, transparent assumptions, and clear relevance to the journal’s aims.
1) Pure Mathematics
Algebra, number theory, geometry, topology, analysis, dynamical systems, and other areas presenting original results with rigorous proofs.
2) Probability and Stochastic Processes
Probability theory, stochastic processes, stochastic differential equations, random graphs, and applications of stochastic modeling.
3) Statistical Theory and Inference
Estimation, hypothesis testing, Bayesian and frequentist inference, asymptotic theory, robustness, and statistical decision theory.
4) Applied Statistics and Multivariate Methods
Regression and generalized models, multivariate analysis, experimental design, sampling, survey methodology, and statistical modeling in applied research.
5) Computational Mathematics and Scientific Computing
Numerical analysis, optimization, computational methods, algorithms, simulation, uncertainty quantification, and reproducible scientific computing.
6) Data Science, Machine Learning, and Statistical Computing
Machine learning methods, statistical learning theory, high-dimensional inference, model selection, computational statistics, and reproducible data pipelines.
7) Time Series, Forecasting, and Econometrics
Time series analysis, forecasting, econometric modeling, causal inference, panel data methods, and applied quantitative methods for decision-making.
8) Biostatistics, Epidemiology, and Risk Modeling
Biostatistical methods, epidemiological modeling, survival analysis, risk assessment, reliability analysis, and statistical methods for health and safety.
9) Statistical Quality Control and Industrial Statistics
Quality control, process monitoring, reliability, Six Sigma, industrial experimentation, and statistical methods for manufacturing and services.
10) Mathematical Modeling and Interdisciplinary Applications
Deterministic and stochastic modeling, optimization, network science, operations research, and mathematical methods applied to science, engineering, and social systems.
Types of Manuscripts Considered
The journal considers, among others, the following manuscript types: theoretical papers with rigorous proofs, methodological and computational studies with reproducible implementation, applied modeling papers with clear assumptions and validation, systematic/scoping reviews, and concise conceptual analyses. Submissions must demonstrate clear problem formulation, transparent methods, robust results, and ethically responsible conduct.
Mikailalsys Journal of Mathematics and Statistics advances mathematical and statistical scholarship by publishing ethically grounded, methodologically robust, and internationally relevant research that strengthens theory, computation, and applied modeling for reliable inference and decision-making.