Wiley Interdisciplinary Reviews: Computational Statistics
© Wiley Periodicals, Inc.
Edited By: James E. Gentle, James M. Landwehr, and David W. Scott
Online ISSN: 1939-0068
The award-winning WIREs (Wiley Interdisciplinary Reviews) series combines some of the most powerful features of encyclopedic reference works and review journals in an innovative online format. They are designed to promote a cross-disciplinary research ethos while maintaining the highest scientific and presentational standards, but should be viewed first and foremost as evolving online databases of cutting-edge reviews.
WIREs Computational Statistics
- An important new forum to promote cross-disciplinary discussion on a broad array of computational and statistical techniques
- An authoritative and encyclopedic resource addressing key topics from the diverse perspective of traditional statistics and modern computation
- Free or low cost access in developing countries through Research4Life
- Indexed by Scopus
For more information, please go to wires.wiley.com/compstats.
Aims and Scope
WIREs Computational Statistics is a major new scientific publication that supports the information needs of researchers in this field and helps to shape its future development. Its goals are:
- to present the current state of the art of Computational Statistics through an ongoing series of commissioned reviews written by leading researchers
- to capture the crucial interdisciplinary flavor of this field by including articles that address the key topics from the differing perspectives of statistics and computing, and including potential applications areas in technology, biology, physics, geography, and sociology
- to capture the rapid development of Computational Statistics through a systematic program of content updates
- and to encourage new participation in this field by presenting its achievements and challenges in an accessible way to a broad audience.
WIREs Computational Statistics will be fully indexed in the major abstracting services, and will be assigned an impact factor in the same way as a journal. WIREs Computational Statistics will offer a comprehensive, coherent, well-structured coverage of the field. It will also be updated in a systematic fashion so that its content remains as current as possible.
WIREs Computational Statistics reviews are structured into different article types:
- Opinions - provide a forum for thought-leaders to offer a more individual perspective
- Overviews - provide a broad and non-technical treatment of important topics suitable for advances students and for researchers without a strong background in the field
- Advanced Reviews - examine key areas of research in a citation-rich format suitable for researchers and advanced students
- Focus Articles - present specific real-world issues, examples and implementations
- Editorial Commentaries - allows WIREs editors to comment on broad research trends in a less formal style
- WIREs Computational Statistics has the following top-level category structure:
- Applications of Computational Statistics
- Artificial Intelligence
- Biostatistics and Bioinformatics
- Computational Bayesian Methods
- Computationally Intensive Statistical Methods
- Computer Science Methods
- Data Mining
- Data Structures
- Data Visualization
- Machine Learning
- Modeling and Simulation
- Numerical Analysis
- Statistical Methods
Each WIREs title was established in response to the urgent need to publish current, comprehensive reviews of the pioneering research that is being done in an interdisciplinary and complementary set of fields. Reviews are structured into different Article Types, each with its own description and intended audience.
Our goal is to support the research and teaching needs of advanced students, scientists, healthcare providers, governmental and policy analysts, and other professionals in these rapidly developing areas with article types catered to different readers, collections on hot topics, and freely available PowerPoint downloads of each article’s figures.
Additionally, Wiley participates in the Research4Life initiative, which provides people at more than 7,700 institutions in the developing world with free or low cost access to scientific content.
Applications of computational statistics; artificial intelligence; biostatistics and bioinformatics; computational Bayesian methods; computationally intensive statistical methods; computer science methods; data mining; data structures; data visualization; databases; machine learning; modeling and simulation; numerical analysis; optimization; statistical methods.
Abstracting and Indexing Information
- COMPENDEX (Elsevier)
- Emerging Sources Citation Index (Thomson Reuters)
- Mathematical Reviews/MathSciNet/Current Mathematical Publications (AMS)
- SCOPUS (Elsevier)
- Web of Science (Thomson Reuters)