Axis Journal of Mathematical Statistics and Modelling
Aim and Scope
Axis Journal of Mathematical Statistics and Modelling publishes original research that advances the theoretical understanding of statistics, probability, and mathematical modelling. The journal supports work grounded in formal reasoning, clear mathematical structure, and logical development of models and methods.
The journal covers a wide range of topics within its core focus areas, including:
- Statistical Inference: Estimation theory, hypothesis testing, decision rules, and the development of inferential frameworks under classical and Bayesian approaches.
- Probability Theory: Properties of random variables, convergence in distribution and probability, dependence structures, and fundamental probabilistic results.
- Stochastic Processes: Theoretical treatment of Markov processes, martingales, renewal processes, and other random evolutions in discrete or continuous time.
- Time Series Analysis: Mathematical approaches to modelling and understanding autocorrelation, stationarity, and spectral behaviour in temporal data.
- Multivariate and High-Dimensional Statistics: Topics including multivariate distributions, matrix decompositions, estimation in high-dimensional settings, and dimensionality reduction.
- Asymptotic Theory: Large-sample properties of estimators and tests, consistency, convergence rates, and asymptotic distributions.
- Mathematical Modelling: Analytical treatment of deterministic or stochastic models expressed through equations, transformations, or structural assumptions.
- Computational and Algorithmic Approaches: Theoretical analysis of simulation techniques, algorithm behaviour, sampling strategies, and convergence in statistical computation.
Submissions must clearly state assumptions, use well-defined notation, and include results supported by proof or logical derivation. Applied examples may be used to motivate a method, but the journal prioritises generality and theoretical contribution over context-specific results.
The journal welcomes articles that offer fresh perspectives on known results, propose alternative formulations, or extend existing methods with greater clarity or generality. Work that challenges established assumptions, highlights boundary cases, or sharpens known conditions is equally welcome.
All manuscripts are reviewed by scholars with expertise in mathematical statistics and related areas. Accepted articles are published open access, making them available to the global academic community without restriction.
Axis Journal of Mathematical Statistics and Modelling serves researchers who value precision, structure, and well-supported argument in statistical and mathematical writing.