Richard M. Royall
- Statistics and Probability top 0.1%
- Ophthalmology top 0.5%
- Radiology, Nuclear Medicine and Imaging top 2%
- Surgery top 5%
- Artificial Intelligence top 2%
- Co-authors
- Jaxk ReevesWilliam G. CumberlandJoanne KatzJonathan C. JavittJames M. TielschHarry A. QuigleyAlfred SommerJay Herson
- Topics
- Statistical Methods and Bayesian Inference (18 papers)Survey Sampling and Estimation Techniques (17 papers)Bayesian Methods and Mixture Models (13 papers)
- Journals
- New England Journal of MedicineJournal of NeuroscienceJournal of the American Statistical Association
- Partner nations
- United StatesTaiwanCanada
In The Last Decade
Richard M. Royall
71 papers receiving 5.7k citations
Hit Papers
Peers
Comparison fields: 5 of 198
- Statistics and Probability 1.7k
- Ophthalmology 965
- Radiology, Nuclear Medicine and Imaging 786
- Surgery 607
- Artificial Intelligence 554
Countries citing papers authored by Richard M. Royall
This map shows the geographic impact of Richard M. Royall'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 Richard M. Royall with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Richard M. Royall more than expected).
Fields of papers citing papers by Richard M. Royall
This network shows the impact of papers produced by Richard M. Royall. 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 Richard M. Royall. The network helps show where Richard M. Royall may publish in the future.
Co-authorship network of co-authors of Richard M. Royall
This figure shows the co-authorship network connecting the top 25 collaborators of Richard M. Royall. A scholar is included among the top collaborators of Richard M. Royall 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 Richard M. Royall. Richard M. Royall is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 11 | |
| 2 | 119 | |
| 3 | 69 | |
| 4 | 101 | |
| 5 | 81 | |
| 6 | 399 | |
| 7 | 3 | |
| 8 | 49 | |
| 9 | 26 | |
| 10 | 129 | |
| 11 | Finite population sampling and inference : a prediction approach | 225 |
| 12 | 139 | |
| 13 | 25 | |
| 14 | 178 | |
| 15 | 128 | |
| 16 | Racial Differences in the Cause-Specific Prevalence of Blindness in East Baltimorebreakdown → | 612 |
| 17 | 150 | |
| 18 | 5 | |
| 19 | 42 | |
| 20 | 5 |
About Richard M. Royall
Richard M. Royall is a scholar working on Statistics and Probability, Critical Care and Intensive Care Medicine and Artificial Intelligence, having authored 73 papers that have together received 6.2k indexed citations. Recurring topics across this work include Statistical Methods and Bayesian Inference (18 papers), Survey Sampling and Estimation Techniques (17 papers) and Bayesian Methods and Mixture Models (13 papers). The work is most often cited by research in Statistics and Probability (1.7k citations), Ophthalmology (965 citations) and Developmental Neuroscience (236 citations). Richard M. Royall has collaborated with scholars based in United States, Taiwan and Canada. Frequent co-authors include Jaxk Reeves, William G. Cumberland, Joanne Katz, Jonathan C. Javitt, James M. Tielsch, Harry A. Quigley, Alfred Sommer, Jay Herson, Thomas M. Brushart and William R. Bell. Their work appears in journals such as New England Journal of Medicine, Journal of Neuroscience and Journal of the American Statistical Association.
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.