Theme Issue Honoring Professor Bradley Efron on His Retirement
A special issue of Stats (ISSN 2571-905X).
Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 78
Special Issue Editor
Interests: Robust methods and exploratory data analysis with applications to physical, chemical, engineering, and biological sciences (Genomics, forensic science, randomized cancer screening trials, spatial data, particle physics experiments)
Special Issue Information
Dear Colleagues,
We are pleased to propose a Special Issue in honor of Stanford Professor Emeritus Bradley Efron.
Professor Efron’s contributions to the field of statistics have revolutionized approaches to statistical inference and data analysis and have made critical scientific insights possible. Among his numerous honors, he was awarded the prestigious National Medal of Science (2005), the Guy Medal in Gold by the Royal Statistical Society (2014), and the International Prize in Statistics (2018). He has also served as Editor of Journal of the American Statistical Association, founded and was Editor-in-Chief of The Annals of Applied Statistics, and was President of the Institute of Mathematical Statistics (1987–1988) and of the American Statistical Association (2004) (Available online: https://efron.ckirby.su.domains/).
This Special Issue, in honor of Professor Bradley Efron, welcomes both manuscripts describing original work and review articles. The Guest Editors invite submissions that focus on areas in which he has worked, including bootstrap, empirical Bayes, large-scale inference, exponential families, and clinical trials. Articles that relate to Professor Efron’s other research interests will also be considered.
Prof. Dr. Karen Kafadar
Guest Editor
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. Stats is an international peer-reviewed open access quarterly 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 1600 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
- Bootstrap
- Empirical Bayes
- Large-Scale Inference
- Exponential Families
- Clinical Trials
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.
Further information on MDPI's Special Issue policies can be found here.