William Barcella
Impact in
- Nephrology top 10%
- Parathyroid Disorders and Treatments
- Statistics and Probability top 10%
- Statistical Methods and Bayesian Inference
- Statistical Methods and Inference
Papers in
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- Bayesian Methods and Mixture Models 5
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- Statistical Methods and Inference 4
- Statistical Methods and Bayesian Inference 3
- Statistical Distribution Estimation and Applications 1
- Co-authors
- Maria De Iorio (7 shared papers)James Malone‐Lee (5 shared papers)Gianluca Baio (3 shared papers)Tushar Kotecha (1 shared paper)Tamer Rezk (1 shared paper)Anthony Kupelian (2 shared papers)Thomas A. Treibel (1 shared paper)Vivek Muthurangu (1 shared paper)
- Journals
- International Urogynecology Journal (2 papers)JACC. Cardiovascular imaging (1 paper)Journal of the Royal Statistical Society Series C (Applied Statistics) (1 paper)Clinical Trials (1 paper)Statistics in Medicine (1 paper)
- Partner nations
- United KingdomUnited StatesItaly
In The Last Decade
William Barcella
10 papers receiving 219 citations
Peers
Comparison fields: 5 of 47
- Nephrology 42
- Statistics and Probability 31
- Urology 17
- Rheumatology 34
- Molecular Biology 115
Countries citing papers authored by William Barcella
This map shows the geographic impact of William Barcella'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 William Barcella with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites William Barcella more than expected).
Fields of papers citing papers by William Barcella
This network shows the impact of papers produced by William Barcella. 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 William Barcella. The network helps show where William Barcella may publish in the future.
Co-authors
The 25 scholars most cited alongside William Barcella, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2018 | 124 | |
| 2 | 2018 | 25 | |
| 3 | 2016 | 19 | |
| 4 | 2017 | 18 | |
| 5 | 2017 | 14 | |
| 6 | 2015 | 13 | |
| 7 | 2017 | 4 | |
| 8 | 2018 | 3 | |
| 9 | 2015 | 2 | |
| 10 | 2016 | 1 | |
| 11 | 2015 | 1 |
About William Barcella
William Barcella is a scholar working on Artificial Intelligence, Statistics and Probability, Political Science and International Relations, Sociology and Political Science and Molecular Biology, having authored 11 papers that have together received 224 indexed citations. Recurring topics across this work include Bayesian Methods and Mixture Models (5 papers), Statistical Methods and Inference (4 papers), Statistical Methods and Bayesian Inference (3 papers), Terrorism, Counterterrorism, and Political Violence (1 paper), Statistical Distribution Estimation and Applications (1 paper), Urinary Tract Infections Management (1 paper), Jewish and Middle Eastern Studies (1 paper) and Islamic Studies and History (1 paper). The work is most often cited by research in Nephrology (42 citations), Statistics and Probability (31 citations), Urology (17 citations), Rheumatology (34 citations) and Molecular Biology (115 citations). William Barcella has collaborated with scholars based in United Kingdom, United States and Italy. Frequent co-authors include Maria De Iorio, James Malone‐Lee, Gianluca Baio, Tushar Kotecha, Tamer Rezk, Anthony Kupelian, Thomas A. Treibel, Vivek Muthurangu, Ana Martinez–Naharro and James Moon. Their work appears in journals such as International Urogynecology Journal, JACC. Cardiovascular imaging, Journal of the Royal Statistical Society Series C (Applied Statistics), Clinical Trials and Statistics in Medicine.
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.