Wiley Interdisciplinary Reviews: Computational Statistics is a leading journal published by WILEY, renowned for its influential contributions to the field of statistics and its application in computational studies. With an impressive impact factor reflected in its 2023 categorization as Q1 in Statistics and Probability, this journal ranks among the top in its category, positioned at 20 out of 278 in Scopus, placing it in the 92nd percentile for its discipline. The journal spans from 2009 to 2024 and offers a rich repository of interdisciplinary insights that encompass both theoretical advancements and practical applications of computational statistics, making it an invaluable resource for researchers, professionals, and students alike. While it does not currently offer open access, the journal's commitment to high-quality, peer-reviewed content ensures that it remains a trusted source for cutting-edge developments and methodologies in the rapidly evolving realm of computational statistics.

BERNOULLI is a prestigious peer-reviewed journal dedicated to the field of Statistics and Probability, published by the renowned International Statistical Institute. Since its inception in 1995, this journal has established itself as a vital resource for researchers and professionals, achieving a remarkable impact factor and consistently ranking in the top quartile (Q1) of its category as of 2023. With a strong presence in the Scopus database, where it ranks #64 among 278 journals in Mathematics, it places in the 76th percentile, underscoring its significance in the academic landscape. Although not an open-access journal, its contributions are pivotal for advancing statistical theory and its applications across various disciplines. As Berounlli continues to evolve until 2024, it remains committed to disseminating high-quality research that fosters innovation and supports the global analytics community. The journal’s scope encompasses a wide range of topics in statistics, including but not limited to theoretical statistics, applied statistics, and data analysis, making it an essential read for anyone engaged in statistical research.

The Journal of Intelligent Information Systems, published by Springer since 1992, is a premier academic journal that offers a multidisciplinary platform in the fields of Artificial Intelligence, Computer Networks and Communications, Hardware and Architecture, Information Systems, and Software. With an impressive impact reflected in its 2023 Q2 category rankings across multiple domains and a commendable standing in the Scopus Rankings—ranking #84 in Computer Networks and Communications and #101 in Artificial Intelligence—the journal is recognized for its contribution to advancing knowledge and innovation. Although it is not an open-access journal, its accessibility through institutional subscriptions ensures that a wide range of researchers, professionals, and students can engage with high-quality, peer-reviewed research that addresses the latest advancements and trends in intelligent systems. For over three decades, this journal has effectively bridged gaps between academia and industry, making it a vital resource for those aiming to push boundaries in intelligent information systems.

STATISTICA NEERLANDICA is a prestigious peer-reviewed journal published by Wiley, focusing on the fields of statistics and probability. Established in 1946 and addressing key issues in statistical theory and its applications, the journal has significantly contributed to the development of modern statistical practices. With an impressive Q2 categorization in both Statistics and Probability, as well as Statistics, Probability, and Uncertainty, STATISTICA NEERLANDICA stands out within its field, ranking in the 62nd percentile among its peers in mathematics, specifically in statistics and probability. Researchers, professionals, and students can benefit from its rigorous scholarship and innovative methodologies, aiding in the advancement of statistical science. Although the journal does not operate under an open access model, it maintains a commitment to disseminating high-quality research, making it a vital resource for those engaged in statistical inquiry.

Communications in Mathematics and Statistics, published by Springer Heidelberg, is a prominent journal dedicated to advancing research in the fields of applied mathematics, computational mathematics, and statistics. With an ISSN of 2194-6701 and an E-ISSN of 2194-671X, the journal has established itself as a vital platform for interdisciplinary scholarly communication since its inception in 2013. The journal falls within the third quartile in various rankings including applied mathematics, computational mathematics, and statistics and probability, indicating its solid position in the global research landscape. With a focus on innovative methodologies and practical applications, Communications in Mathematics and Statistics aims to bridge the gap between theoretical research and practical implementation. Researchers, professionals, and students alike will find valuable insights and cutting-edge studies that contribute to the evolution of mathematical sciences. The journal is based in Germany, with a commitment to fostering international collaboration and accessibility in mathematical research.

SIAM Journal on Mathematics of Data Science is an esteemed publication within the fields of applied mathematics and data science, published by SIAM PUBLICATIONS. This journal serves as a vital platform for researchers and practitioners, dedicated to disseminating high-quality research that addresses complex mathematical problems arising in the context of data science. The journal aims to bridge the gap between rigorous mathematical theory and practical applications, fostering interdisciplinary collaboration among mathematicians, data scientists, and statisticians. With its commitment to excellence, the SIAM Journal on Mathematics of Data Science contributes significantly to advancing the understanding and development of mathematical methodologies that analyze and interpret large datasets effectively. Researchers and professionals will find it an invaluable resource with its comprehensive articles, insightful reviews, and original research papers, which represent the forefront of innovative mathematical approaches in the evolving landscape of data science. For those interested in contributing to this dynamic field, the journal provides an array of access options tailored to diverse audiences.

STATISTICA SINICA, published by the esteemed STATISTICA SINICA organization, stands as a premier journal in the fields of Statistics and Probability, boasting a significant impact within the academic community. With an ISSN of 1017-0405 and E-ISSN of 1996-8507, this journal has evolved from its inception in 1996, continuing to publish cutting-edge research through 2024. As recognized by its recent categorization in Q1 quartiles in both Statistics and Probability and Statistics, Probability and Uncertainty for 2023, it ranks among the top journals in its discipline, meriting attention from researchers and practitioners alike. Despite lacking open access options, it delivers rigorous, peer-reviewed articles that contribute to the advancement of statistical science. With its base in Taiwan, and a dedicated editorial team located at the Institute of Statistical Science, Academia Sinica, Taipei, STATISTICA SINICA continues to be a vital resource for statisticians, data scientists, and related professionals seeking innovative methodologies and insights within this dynamic field.

Theoretical Computer Science, published by Elsevier, serves as a pivotal platform in the field of computational theory, exploring the foundational aspects of computer science and mathematical logic since its inception in 1975. With both a print ISSN of 0304-3975 and an E-ISSN of 1879-2294, this journal is esteemed for its rigorous peer-review process and commitment to advancing knowledge in theoretical frameworks and algorithms. Positioned in the Q2 quartile for both Computer Science (miscellaneous) and Theoretical Computer Science categories, it ranks #124 out of 232 in general computer science and #73 out of 130 in theoretical computer science according to Scopus metrics, reflecting its significant influence and reach within the academic community. Researchers and professionals can access this journal through institutional subscriptions, providing a plethora of high-quality articles that contribute to ongoing debates and developments in the discipline. The journal's scope encompasses a wide array of topics, ensuring relevance across various subfields, thus making it an essential resource for anyone dedicated to furthering their understanding of theoretical computer science.

Statistics and Its Interface, issn 1938-7989, published by INT PRESS BOSTON, INC, is a vital academic journal dedicated to bridging the critical intersection of statistics, applied mathematics, and interdisciplinary research. With its inaugural publication in 2011, this journal has continually aimed to provide a platform for innovative statistical methods and their application across various fields, offering valuable insights for researchers and practitioners alike. While the journal currently operates without an open access model, it maintains an essential position within the scholarly community, evidenced by its 2023 rankings in the third quartile for Applied Mathematics and the fourth quartile for Statistics and Probability. Furthermore, it holds a respectable position in Scopus rankings, reflecting its commitment to quality over quantity. By publishing cutting-edge research, Statistics and Its Interface serves as a critical resource for advancing statistical knowledge and cultivating a deeper understanding of its applications in real-world contexts.

ACM Transactions on Knowledge Discovery from Data (TKDD), published by the Association for Computing Machinery, is a prestigious journal at the forefront of the interdisciplinary realm of data mining and knowledge discovery. With an impressive Q1 ranking in Computer Science and a Scopus rank of #43 out of 232, this journal stands out as a top-tier resource for innovative research that addresses complex challenges in data science. Covering impactful studies from 2007 to 2024, TKDD presents cutting-edge algorithms, methodologies, and applications that shape the future of knowledge extraction from vast datasets. While not an open-access journal, it provides a platform for researchers, professionals, and students to disseminate their findings and engage with the latest advancements in this rapidly evolving field. By fostering collaboration and knowledge sharing, TKDD plays a vital role in advancing the understanding and application of data analysis techniques, making it an essential read for anyone involved in the pursuit of knowledge from data.