Research Area

Mathematical Applications and Statistical Rigor (MASR) 

Publisher: Scientific Innovation Research Group (SIRG)

Mathematical Applications and Statistical Rigor (MASR) provides a premier forum for high-quality research that demonstrates how theoretical insight and methodological rigor directly inform and elevate applied problem-solving. The journal welcomes contributions that either introduce novel mathematical and statistical frameworks with clear application pathways, or present applied studies that generate meaningful theoretical advances.

MASR welcomes original contributions spanning theoretical, applied, and computational mathematics and statistics, with strong emphasis on their integration. Areas of interest include, but are not limited to:

1. Methodological and Theoretical Advances

  • Optimization, simulation, uncertainty quantification, causal inference, and Bayesian methodology

  • High-dimensional statistics, statistical learning theory, experimental design, and inference

  • Nonlinear, algebraic, operator, differential, and stochastic equations

  • Numerical analysis, linear algebra, approximation theory, multivariate analysis, and integration

2. Computational and Data-Intensive Science

  • Machine learning and artificial intelligence with rigorous statistical guarantees

  • Scalable mathematical and statistical methods for large-scale and complex data

  • Inverse problems, discrete and infinite-dimensional systems

  • Methods for noisy, incomplete, and high-uncertainty data

  • Mathematical and statistical foundations of quantum computing

3. Interdisciplinary and Applied Sciences

  • Biological and Medical Sciences: systems biology, genomics, epidemiology, clinical trial design, medical imaging, and health informatics

  • Engineering and Physical Sciences: computational mechanics, fluid dynamics, materials informatics, and quantum algorithms

  • Finance and Economics: quantitative risk modeling, econometrics, financial time series, and algorithmic trading

  • Environmental and Earth Sciences: climate modeling, spatial statistics, geostatistics, and remote sensing

  • Humanities and Social Sciences: statistical, network, and computational models applied to social, linguistic, and historical data