Mathematical and Computational Statistics and Their Applications
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Probability and Statistics".
Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 25848
Special Issue Editors
Interests: multivariate analysis; likelihood inference and estimation; exact and near-exact distributions; computational statistics; linear, generalized linear and mixed linear models
Special Issue Information
Dear Colleagues,
In this Special Issue we are looking for high-quality research papers in almost all areas of statistics. Although emphasis is placed on estimation, inferential methods and multivariate analysis, thorough reviews on topics of current and wide interest are also welcome, mainly those that may provide commentaries leading to open perspectives of new applications in well-known research topics.
Submissions must be rigorous, clear, well written in good English, and be accessible and appealing to a broad audience.
There is no restriction on the length of the papers, nor on the use of color figures and diagrams.
If thought to be adequate, electronic files with software or computer code, full details of calculations, and full description of experimental procedures, lengthy datasets, or detailed proofs that may be judged to be too lengthy to be inserted in the body of the paper may be added as supplementary materials.
Particularly welcome are submissions that focus on relevant new methodology developed to solve real-life problems, as well as those focusing on specially innovative applications that are expected to have a broad scientific impact and interest a broad audience, concerning the analysis and interpretation of data, namely multidimensional data, as well as applications that may foster the development and application of statistical methods on the interface with other scientific areas such as health sciences, medicine, biology, genetics, hydrology, econometry, actuarial science, social, environmental, and life sciences, eventually leading to new horizons or new policies.
Prof. Dr. Carlos Agra Coelho
Prof. Dr. Tatjana von Rosen
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- High-dimensional methods for estimation, hypotheses testing, and data analysis
- Mathematical statistics and multivariate analysis
- Likelihood-related methods (estimation and inference)
- Extreme-value theory and applications
- Spatial statistics
- Statistical disclosure control and data protection
- Reliability and maintainability issues and related disciplines
- Risk assessment and management
- Financial mathematics and statistics
- Econometrics
- Time series analysis
- Inferential methods and hypothesis testing
- Data mining and machine learning
- Computational statistics
- Functional data analysis
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