Model Assisted Statistics and Applications (MASA)

Model Assisted Statistics and Applications is a quarterly, peer-reviewed academic journal that serves as a platform for advanced research in model-assisted statistical methodology and applications. The journal emphasizes the use of correlated auxiliary information or underlying theoretical/design models to improve statistical inference and analysis across a wide variety of statistical disciplines. This includes foundational and applied research in sampling theory, econometric modeling, time series analysis, design of experiments, and multivariate statistical techniques — with both applied and theoretical perspectives. The scope of the journal bridges statistical theory and practice: articles might present new methods for survey estimation using auxiliary data, innovative models for time series forecasting, experimental design strategies in complex systems, or robust multivariate estimation techniques under realistic assumptions. The journal’s interdisciplinary appeal attracts contributions from statisticians, mathematicians, and applied researchers who tackle real-world problems using rigorous statistical tools. Over time, the journal’s publication and distribution have transitioned to SAGE Publications, increasing its international visibility and accessibility through modern platforms and indexing services like Scopus. Model Assisted Statistics and Applications continues to contribute to the development of statistical science by highlighting methodological innovations that enhance the quality and efficiency of statistical analysis in data-rich environments.

Model Assisted Statistics and Applications (MASA) is :-

• International, Peer-Reviewed, Open Access, Refereed, General Mathematics, Modelling and Simulation, Statistics and Probability, improving estimation, design, adjustment, sampling theory, econometrics, time series analysis, design of experiments, multivariate analysis, related applied and theoretical statistical work. , Online or Print , Quarterly Journal

• UGC Approved, ISSN Approved: P-ISSN P-ISSN: 1574-1699, E-ISSN: 1875-9068, Established: 2005,
• Does Not Provide Crossref DOI

• Not indexed in Scopus, WoS, DOAJ, PubMed, UGC CARE