Focus and Scope

Global Journal of Statistics (GJSTA) is a peer-reviewed academic journal that publishes original, high-quality, and rigorously reviewed research in statistics and data science. The journal prioritizes contributions that demonstrate strong theoretical innovation, robust methodological development, and empirical relevance to contemporary statistical challenges at national, regional, and global levels.

The journal welcomes theoretical, methodological, and applied research that advances statistical science and its applications across disciplines. Particular emphasis is placed on studies that integrate statistics with data science, machine learning, artificial intelligence, and evidence-based decision-making.

GJSTA aims to advance statistical knowledge by promoting reproducible research, encouraging methodological innovation, and supporting the application of statistical methods to real-world problems. The journal positions itself as a platform for high-impact scholarship with strong international visibility and citation potential.

Scope

1. Statistical Theory

  • Probability theory and stochastic processes
  • Statistical inference and estimation theory
  • Asymptotic theory and distribution theory

2. Applied Statistics

  • Statistical modeling and data analysis
  • Regression analysis and multivariate methods
  • Experimental design and survey methods
  • Time series analysis and forecasting

3. Bayesian Statistics

  • Bayesian inference and computation
  • Hierarchical and multilevel models
  • Bayesian decision theory

4. Computational Statistics

  • Simulation methods and Monte Carlo techniques
  • Numerical methods in statistics
  • Statistical computing and algorithms

5. Data Science and Machine Learning

  • Statistical learning and predictive modeling
  • Classification and clustering methods
  • Big data analytics
  • Artificial intelligence and statistical methods

6. Biostatistics and Health Statistics

  • Clinical trials and medical statistics
  • Epidemiological methods
  • Public health data analysis

7. Econometrics and Financial Statistics

  • Econometric modeling and estimation
  • Financial data analysis and risk modeling
  • Panel data and time series econometrics

8. Environmental and Spatial Statistics

  • Spatial data analysis and geostatistics
  • Environmental modeling and climate data
  • Spatio-temporal analysis

9. Industrial and Quality Statistics

  • Quality control and reliability analysis
  • Process optimization and industrial statistics
  • Statistical methods in engineering

10. Statistical Applications and Interdisciplinary Research

  • Statistics in social sciences and humanities
  • Statistics in business and economics
  • Interdisciplinary data-driven research
  • Policy evaluation and impact analysis

Editorial Emphasis

  • Clear research gap and originality
  • Strong theoretical and methodological contribution
  • Reproducibility and transparency of analysis
  • Use of advanced statistical techniques
  • International relevance and interdisciplinary impact
  • Use of recent and high-quality international references