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Statistical Theory and Related Fields - Routledge | 2024 Impact Factor:1.3 | Cite Score:1.2 | Q3

Statistical Theory and Related Fields Journal With Cite Score

Cite Score and Journal Rank of Statistical Theory and Related Fields

  • About: Statistical Theory and Related Fields Journal is an academic, peer-reviewed journal that focuses on the development and application of statistical theories and methodologies. It serves as a platform for researchers, academics, and practitioners to publish original research that contributes to the advancement of statistical science and its related fields.
  • Objective: The primary objective of Statistical Theory and Related Fields Journal is to foster the development of statistical theory and promote its application across various scientific disciplines. The journal aims to disseminate high-quality research that enhances the understanding of statistical principles, methods, and their practical applications in addressing complex real-world problems. It encourages the exploration of new statistical models, algorithms, and computational techniques.
  • Interdisciplinary Scope: Statistical Theory and Related Fields Journal encompasses a wide range of topics that highlight the interdisciplinary nature of statistics. Key areas of focus include: Statistical Theory: Research on fundamental statistical concepts, including probability theory, inferential methods, hypothesis testing, and estimation techniques. Applied Statistics: Studies that apply statistical methodologies to various fields such as medicine, biology, engineering, economics, and social sciences, demonstrating the practical utility of statistical tools. Computational Statistics: Exploration of algorithms, computational methods, and software development for statistical analysis, including machine learning techniques and data mining. Bayesian Statistics: Research on Bayesian approaches to statistical inference, model selection, and decision-making processes. Multivariate Analysis: Studies on techniques for analyzing multivariate data, including principal component analysis, factor analysis, and multidimensional scaling.
  • Impact and Significance: Statistical Theory and Related Fields Journal plays a crucial role in advancing the field of statistics by providing a forum for the publication of cutting-edge research. The journals commitment to rigorous peer review and interdisciplinary collaboration ensures the dissemination of innovative methodologies and applications that address diverse scientific challenges.

  • Editor-in-Chief:   Jun Shao

  • Scope: Statistical Theory and Related Fields Journal is an academic journal dedicated to publishing high-quality research articles and reviews in the field of statistical theory and its various related areas. The journal aims to provide a platform for statisticians, mathematicians, and researchers to present their theoretical advancements, methodologies, and applications in statistics.
    1. Statistical Theory: Fundamental research on statistical theory, including probability theory, inference, estimation, hypothesis testing, and asymptotic theory. The journal focuses on new developments in the theoretical underpinnings of statistical methods.
  • 2. Mathematical Statistics: Articles that explore the mathematical foundations of statistics, covering topics like stochastic processes, multivariate analysis, non-parametric methods, and robust statistics. Research that advances mathematical techniques for statistical analysis is also featured.
  • 3. Applied Statistics: Research that applies statistical theory to real-world problems in various fields such as economics, engineering, medicine, biology, social sciences, and environmental sciences. The journal emphasizes the development of innovative statistical methodologies for practical applications.
  • 4. Computational Statistics: Studies focusing on computational methods in statistics, including algorithm development, simulation techniques, and data analysis tools. Topics like Monte Carlo methods, Bayesian computation, and machine learning algorithms are frequently covered.
  • 5. Statistical Modelling: Papers that discuss new models for data analysis, including linear and non-linear models, generalized linear models, time series analysis, and spatial statistics. The journal encourages submissions that introduce novel statistical models and their applications.
  • 6. Bayesian Statistics: Articles on Bayesian theory and methods, including Bayesian inference, Bayesian networks, hierarchical models, and applications of Bayesian approaches in various scientific fields. Research on advances in Bayesian computational methods is also welcome.
  • 7. Data Science and Big Data: Research that intersects with data science, focusing on statistical techniques for analyzing large datasets, data mining, and predictive modeling. The journal includes studies on the integration of statistical methods with big data technologies and analytics.
  • 8. Biostatistics: Studies on the application of statistical methods to biological and medical research. Topics include clinical trials, survival analysis, epidemiology, and bioinformatics. The journal encourages submissions that address statistical challenges in biomedical research.
  • 9. Statistical Learning and Machine Learning: Research on statistical methods in machine learning, covering supervised and unsupervised learning, feature selection, classification, and regression techniques. Articles that develop statistical learning theories and methods are featured.
  • 10. Multivariate Analysis: Papers that address the theory and methods of multivariate statistical analysis, including principal component analysis, factor analysis, canonical correlation analysis, and clustering techniques. The journal promotes research on new methodologies and applications in multivariate analysis.
  • 11. Nonparametric and Semiparametric Methods: Research focusing on nonparametric and semiparametric inference, including density estimation, regression, and survival analysis. The journal invites articles that propose new methods and theoretical advancements in this area.
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  • Print ISSN:  24754269

    Electronic ISSN:  24754277

  • Abstracting and Indexing:  Scopus

  • Imapct Factor 2024:  1.3

  • Subject Area and Category:   Computer Science, Computational Theory and Mathematics, Decision Sciences, Statistics, Probability and Uncertainty, Mathematics, Analysis, Applied Mathematics, Statistics and Probability

  • Publication Frequency:  

  • H Index:  9

  • Best Quartile:

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    Q3:  Analysis

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  • Cite Score:  1.2

  • SNIP:  0.563

  • Journal Rank(SJR):  0.319