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Mathematical Statistics and Learning - European Mathematical Society Publishing House | 2024 Cite Score: 3.9 | Q1

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Mathematical Statistics and Learning - European Mathematical Society Publishing House | 2024 Cite Score: 3.9 | Q1

  • About this Journal:
  • Mathematical Statistics and Learning is a peer-reviewed scholarly journal that focuses on the theoretical foundations and methodological advancements in statistics, probability, and machine learning.Mathematical Statistics and Learning is devoted to research articles of the highest quality in all aspects of mathematical statistics and learning, including those studied in traditional areas of statistics and in machine learning as well as in theoretical computer science and signal processing.
  • It provides a platform for cutting-edge research that connects rigorous mathematical theory with modern statistical learning and data science applications.
  • The journal publishes original research articles, methodological contributions, theoretical analyses, and review papers.
  • It aims to bridge the gap between classical statistical theory and emerging learning-based approaches used in high-dimensional, complex, or data-intensive environments.
  • Suitable for researchers in mathematics, applied statistics, computer science, machine learning, and related quantitative disciplines.

  • Editor-in-Chief:  

  • Scope of the Journal:
  • Mathematical foundations of statistics and statistical inference.
  • Theoretical analysis of machine learning algorithms and statistical learning theory.
  • High-dimensional statistics, sparsity methods, and regularization techniques.
  • Probability theory, stochastic processes, and their applications in learning.
  • Optimization methods for statistical and machine learning models.
  • Nonparametric and semiparametric models, kernel methods, and empirical process theory.
  • Bayesian statistics, probabilistic modeling, and uncertainty quantification.
  • Applications of mathematical statistics in data science, AI, and complex systems.
  • Latest Research Topics for PhD in Computer Science
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  • Print ISSN:  25202316

    Electronic ISSN:  25202324

  • Abstracting and Indexing:  Scopus

  • Imapct Factor :  

  • Subject Area and Category:  Computational Theory and Mathematics, Signal Processing

  • Publication Frequency:  

  • H Index:  9

  • Best Quartile:

    Q1:  Computational Theory and Mathematics

    Q2:  

    Q3:  

    Q4:  

  • Cite Score:  3.9

  • SNIP:  1.327

  • Journal Rank(SJR):  1.953