Rupert G. Miller
- Statistics and Probability top 0.05%
- Molecular Biology top 5%
- Neurology top 0.5%
- Surgery top 2%
- Physiology top 2%
- Co-authors
- David L. WeeksDavid SiegmundJerry HalpernJ. D. EnglandGabrielle E. KellyT A RaffinGary M. FranklinG. Gronseth
- Topics
- Statistical Methods and Inference (10 papers)Advanced Statistical Methods and Models (8 papers)Statistical Methods and Bayesian Inference (5 papers)
- Journals
- New England Journal of MedicineJournal of the American Statistical AssociationThe Journal of Immunology
- Partner nations
- United StatesUnited KingdomItaly
In The Last Decade
Rupert G. Miller
70 papers receiving 13.4k citations
Hit Papers
Peers
Comparison fields: 5 of 235
- Statistics and Probability 2.9k
- Molecular Biology 1.4k
- Neurology 1.3k
- Surgery 1.2k
- Physiology 1.0k
Countries citing papers authored by Rupert G. Miller
This map shows the geographic impact of Rupert G. Miller's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Rupert G. Miller with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rupert G. Miller more than expected).
Fields of papers citing papers by Rupert G. Miller
This network shows the impact of papers produced by Rupert G. Miller. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Rupert G. Miller. The network helps show where Rupert G. Miller may publish in the future.
Co-authorship network of co-authors of Rupert G. Miller
This figure shows the co-authorship network connecting the top 25 collaborators of Rupert G. Miller. A scholar is included among the top collaborators of Rupert G. Miller based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Rupert G. Miller. Rupert G. Miller is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 145 | |
| 2 | 236 | |
| 3 | 173 | |
| 4 | 55 | |
| 5 | 24 | |
| 6 | 5 | |
| 7 | 105 | |
| 8 | 4 | |
| 9 | 13 | |
| 10 | 77 | |
| 11 | 94 | |
| 12 | Survival Analysisbreakdown → | 2628 |
| 13 | 19 | |
| 14 | 109 | |
| 15 | Cellular immunity in infectious mononucleosis. II. Specific reactivity to Epstein-Barr Virus antigens and correlation with clinical and hematologic parameters. | 19 |
| 16 | 80 | |
| 17 | 85 | |
| 18 | 1 | |
| 19 | 2 | |
| 20 | 3 |
About Rupert G. Miller
Rupert G. Miller is a scholar working on Statistics and Probability, Management Science and Operations Research and Statistics, Probability and Uncertainty, having authored 73 papers that have together received 14.8k indexed citations. Recurring topics across this work include Statistical Methods and Inference (10 papers), Advanced Statistical Methods and Models (8 papers) and Statistical Methods and Bayesian Inference (5 papers). The work is most often cited by research in Statistics and Probability (2.9k citations), Statistics, Probability and Uncertainty (762 citations) and Neurology (1.3k citations). Rupert G. Miller has collaborated with scholars based in United States, United Kingdom and Italy. Frequent co-authors include David L. Weeks, David Siegmund, Jerry Halpern, J. D. England, Gabrielle E. Kelly, T A Raffin, Gary M. Franklin, G. Gronseth, James F. Howard and L. J. Kinsella. Their work appears in journals such as New England Journal of Medicine, Journal of the American Statistical Association and The Journal of Immunology.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.