Consulta las condiciones de publicación y los listados de títulos de los diferentes Acuerdos vigentes en la UBU para publicar en acceso abierto.
Springer
Computational Statistics
Computational Statistics (CompStat) is an international journal that fosters the publication of applications and methodological research in the field of computational statistics. The journal provides a forum for computer scientists, mathematicians, and statisticians working in a variety of areas in statistics, including biometrics, econometrics, data analysis, graphics, simulation, algorithms, knowledge-based systems, and Bayesian computing.
CompStat papers emphasize the contribution to and influence of computing on statistics and vice versa. The journal also publishes package reports.
Officially cited as: Comput Stat
- Fosters the publication of applications and methodological research in the field of computational statistics
- Emphasizes the contribution to and influence of computing on statistics and vice versa
- Provides a forum for computer scientists, mathematicians, and statisticians working in a variety of areas in statistics
- Publishes package reports and software articles
Applied Mathematics & Optimization
The Applied Mathematics and Optimization Journal covers a broad range of mathematical methods in particular those that bridge with optimization and have some connection with applications. Papers considered for publication must contain significant contributions and applications from a mathematical perspective. Core topics include calculus of variations, partial differential equations, stochastic control, optimization of deterministic or stochastic systems in discrete or continuous time, homogenization, control theory, mean field games, dynamic games and optimal transport. Algorithmic, data analytic, machine learning and numerical methods which support the modeling and mathematical analysis of optimization problems are encouraged. Of great interest are papers which show some novel idea in either the theory or model and include some connection with potential applications in science and engineering.
- Presents papers that embrace a wide diversity of applied areas, such as physical, chemical, biological, environmental topics
- Papers that emphasize modeling, applications or algorithms are welcome but must illustrate novel and significant development of underlying mathematics
Statistics in Bioscience
Statistics in Biosciences (SIBS) is published three times a year in print and electronic form. It aims at development and application of statistical methods and their interface with other quantitative methods, such as computational and mathematical methods, in biological and life science, health science, and biopharmaceutical and biotechnological science.
SIBS publishes scientific papers and review articles in four sections, with the first two sections as the primary sections. Original Articles publish novel statistical and quantitative methods in biosciences. The Bioscience Case Studies and Practice Articles publish papers that advance statistical practice in biosciences, such as case studies, innovative applications of existing methods that further understanding of subject-matter science, evaluation of existing methods and data sources. Review Articles publish papers that review an area of statistical and quantitative methodology, software, and data sources in biosciences. Commentaries provide perspectives of research topics or policy issues that are of current quantitative interest in biosciences, reactions to an article published in the journal, and scholarly essays. Substantive science is essential in motivating and demonstrating the methodological development and use for an article to be acceptable. Articles published in SIBS share the goal of promoting evidence-based real world practice and policy making through effective and timely interaction and communication of statisticians and quantitative researchers with subject-matter scientists in biosciences.
- Presents development of statistical methods in quantitative biosciences
- Covers important data applications of statistical methods or computational algorithms
- Emphasized areas are statistics for health/life sciences, biomarkers and precision medicine, neuroimaging statistics, big or high-dimensional data, genetics/genomics statistics, statistical methods and designs for clinical trials
Revista Matemática Complutense
Revista Matemática Complutense is an international research journal supported by the School of Mathematics at Complutense University in Madrid. It publishes high quality research and survey articles across pure and applied mathematics. Fields of interests include: analysis, differential equations and applications, geometry, topology, algebra, statistics, computer sciences and astronomy. This broad interest is reflected in our interdisciplinary editorial board which is comprised of over 30 internationally esteemed researchers in diverse areas.
The Editorial Board of Revista Matemática Complutense organizes the “Santaló Lecture”, a yearly event where a distinguished mathematician is invited to present a lecture at Complutense University and contribute to the journal. Past lecturers include: Charles T.C. Wall, Jack K. Hale, Hans Triebel, Marcelo Viana, Narayanswamy Balakrishnan, Nigel Kalton, Alfio Quarteroni, David E. Edmunds, Giuseppe Buttazzo, Juan L. Vázquez, Eduard Feireisl, Nigel Hitchin, Lajos Horváth, Hélène Esnault, Luigi Ambrosio, Ignacio Cirac and Bernd Sturmfels. The Santaló Lecturer for 2019 will be Noel Cressie from National Institute for Applied Statistics Research Australia (NIASRA), University of Wollongong.
Revista de la Real Academia de Ciencias Exactas, Físicas y Naturales. Serie A. Matemáticas
The journal publishes, in English language only, high-quality Research Articles covering Algebra; Applied Mathematics; Computational Sciences; Geometry and Topology; Mathematical Analysis; Statistics and Operations Research. Also featured are Survey Articles in every mathematical field.
The journal is aware of the generalized gender gap that exists in the authorship of the mathematics research publications. For this reason, it wishes to emphasize what has been and is its unconditional support for equal opportunities and treatment addressed to all its authors.
Communications in Mathematics and Statistics
Communications in Mathematics and Statistics is a peer reviewed international journal published by Springer-Verlag in collaboration with the School of Mathematical Sciences, University of Science and Technology of China (USTC). The journal will be committed to publish high level original peer reviewed research papers in various areas of mathematical sciences, including pure mathematics, applied mathematics, computational mathematics, and probability and statistics. Typically one volume is published each year, and each volume consists of four issues.
Wiley
International statistical review
Aims and Scope
International Statistical Review is the flagship journal of the International Statistical Institute (ISI) and of its family of Associations. It publishes papers of broad and general interest in statistics and probability. The term Review is to be interpreted broadly. The types of papers that are suitable for publication include (but are not limited to) the following:
• reviews/surveys of significant developments in theory, methodology, statistical computing and graphics, statistical education, and application areas;
• tutorials on important topics;
• expository papers on emerging areas of research or application;
• papers describing new developments and/or challenges in relevant areas;
• papers addressing foundational issues;
• papers on the history of statistics and probability;
• white papers on topics of importance to the profession or society; and
• historical assessment of seminal papers in the field and their impact.
Papers must be of interest to a sufficiently broad spectrum of the members of ISI and its family of Associations, who include researchers and practitioners in academia, government, business, and industry. Selected papers will be published with discussions and a rejoinder by the author(s).
The journal will also publish interviews with statisticians who have made prominent contributions to applications, research, and to the development of the profession. Proposals should be sent to the Editors-in-Chief, Scott Holan or Marianthi Markatou.
Readership
Of interest to statistical experts, students and government departments.
Studies in applied mathematics
Overview
Studies in Applied Mathematics (formerly The Journal of Mathematics and Physics before 1969) was founded by Clarence Moore at MIT in 1921. It has a long tradition of facilitating interactions between mathematics and applied disciplines. Its former managing editors were Clarence Moore (1921-1928), Philip Franklin (1929-1945), Eric Reissner (1946-1967), and David Benney (1968-2013). Under David Benney the journal's name was changed and the journal logo was created.
Currently the Journal publishes eight issues per year (in January, February, April, May, July, August, October and November).
Aims and Scope
Studies in Applied Mathematics explores the interplay between mathematics and the applied disciplines. It publishes papers that advance the understanding of physical processes, or develop new mathematical techniques applicable to physical and real-world problems. Its main themes include (but are not limited to) nonlinear phenomena, mathematical modeling, integrable systems, asymptotic analysis, inverse problems, numerical analysis, dynamical systems, scientific computing and applications to areas such as fluid mechanics, mathematical biology, and optics.
Statistical analysis and data mining
Aims and Scope
Statistical Analysis and Data Mining addresses the broad area of data analysis, including statistical approaches, machine learning, data mining, and applications. Topics include statistical and computational approaches for analyzing massive and complex datasets, novel statistical and/or machine learning methods and theory, and state-of-the-art applications with high impact. Of special interest are articles that describe innovative analytical techniques, and discuss their application to real problems, in such a way that they are accessible and beneficial to domain experts across science, engineering, and commerce.
The focus of the journal is on papers which satisfy one or more of the following criteria:
- Solve data analysis problems associated with massive, complex datasets
- Develop innovative statistical approaches, machine learning algorithms, or methods integrating ideas across disciplines, e.g., statistics, computer science, electrical engineering, operation research.
- Formulate and solve high-impact real-world problems which challenge existing paradigms via new statistical and/or computational models
- Provide survey to prominent research topics
The goals of this interdisciplinary journal are to encourage new ideas leveraging strengths from multiple disciplines, communication of novel statistical and machine learning/data mining techniques to both novices and experts involved in the analysis of data from practical problems, principled and effective solutions to real-world large-scale problems, and new research questions rising from deep interactions between methodology and application.
The 21st Century has become a Century of Data, with most domains striving for useful general models for their mountains of data. Statistical analysis, machine learning, and data mining are amongst the most effective bodies of methodology and technology capable of producing useful general models from massive, complex datasets.
Guidelines for Reviewers
1) Scan the paper to identify the key contribution, if any.
2) If the key contribution is minor, reject the paper.
3) If the key contribution is substantial:
a) Synopsize the main ideas of the paper in your own summary;
b) If you know of any closely related research not covered in the paper, mention it;
c) Check the paper for accuracy and note corrections;
d) Check the paper for clarity and suggest alternative wordings where appropriate;
e) If you find the paper incomplete, consider writing your own publishable comment.
4) Do not hesitate to ask the Associate Editor to obtain any of the following, as needed:
a) Reference papers not easily accessible;
b) Original source data;
c) Original code.
5) Make your recommendation for:
a) Acceptance – paper publishable as is;
b) Minor Revision – no serious errors;
c) Major Revision – poorly written or containing potentially correctable flaws;
d) Rejection – paper would need to be totally rewritten or should be abandoned as a bad idea.
Readership
Statistical Analysis and Data Mining seeks to inform upper-level undergraduates and postgraduate students, teachers and researchers in data intensive fields, and scientists and research managers in industry that focus on data; moreover, Statistical Analysis and Data Mining includes reviews and tutorial-like articles that are especially useful for individuals entering data science fields.
Mathematical Methods in the Applied Sciences
Aims and Scope
Mathematical Methods in the Applied Sciences publishes papers dealing with new mathematical methods for the consideration of linear and non-linear, direct and inverse problems for physical relevant processes over time- and space- varying media under certain initial, boundary, transition conditions etc. Papers dealing with biomathematical content, population dynamics and network problems are most welcome.
Mathematical Methods in the Applied Sciences is an interdisciplinary journal: therefore, all manuscripts must be written to be accessible to a broad scientific but mathematically advanced audience. All papers must contain carefully written introduction and conclusion sections, which should include a clear exposition of the underlying scientific problem, a summary of the mathematical results and the tools used in deriving the results. Furthermore, the scientific importance of the manuscript and its conclusions should be made clear. Papers dealing with numerical processes or which contain only the application of well established methods will not be accepted.
Because of the broad scope of the journal, authors should minimize the use of technical jargon from their subfield in order to increase the accessibility of their paper and appeal to a wider readership. If technical terms are necessary, authors should define them clearly so that the main ideas are understandable also to readers not working in the same subfield.
Readership
Applied mathematicians in industry and research · educators in applied mathematics and applied sciences.