Jon Wakefield
- Statistics and Probability top 0.2%
- Statistical Methods and Bayesian Inference 39
- Statistical Methods in Clinical Trials 17
- Statistical Methods and Inference 11
- Modeling and Simulation top 1%
- COVID-19 epidemiological studies 10
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- Air Quality and Health Impacts 13
- Health top 2%
- Genetics top 2%
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- Data-Driven Disease Surveillance 15
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- Bayesian Methods and Mixture Models 14
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- Spatial and Panel Data Analysis 14
- Co-authors
- Paul ElliottJoshua M. AkeySerge Aleshin‐GuendelVictoria KnutsonWilliam MsemburiAriel KarlinskyStephen WalkerSomnath Chatterji
- Journals
- Statistics in Medicine (15 papers)Biometrics (11 papers)Journal of the Royal Statistical Society Series A (Statistics in Society) (8 papers)
- Partner nations
- United StatesUnited KingdomCanada
In The Last Decade
Jon Wakefield
130 papers receiving 5.2k citations
Hit Papers
Peers
Comparison fields: 5 of 208
- Statistics and Probability 1.0k
- Modeling and Simulation 238
- Health, Toxicology and Mutagenesis 574
- Health 300
- Genetics 789
Countries citing papers authored by Jon Wakefield
This map shows the geographic impact of Jon Wakefield'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 Jon Wakefield with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jon Wakefield more than expected).
Fields of papers citing papers by Jon Wakefield
This network shows the impact of papers produced by Jon Wakefield. 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 Jon Wakefield. The network helps show where Jon Wakefield may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Jon Wakefield, 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 | 2025 | 1 | |
| 2 | 2024 | 0 | |
| 3 | 2023 | 1 | |
| 4 | 2022 | 1 | |
| 5 | 2020 | 14 | |
| 6 | 2020 | 16 | |
| 7 | 2017 | 102 | |
| 8 | Restricted covariance priors with applications in spatial statistic | 2015 | 7 |
| 9 | 2015 | 52 | |
| 10 | 2014 | 6 | |
| 11 | 2012 | 18 | |
| 12 | 2011 | 130 | |
| 13 | 2008 | 5 | |
| 14 | 2008 | 22 | |
| 15 | 2007 | 10 | |
| 16 | 2003 | 61 | |
| 17 | 2001 | 15 | |
| 18 | 2001 | 7 | |
| 19 | 1999 | 10 | |
| 20 | 1996 | 120 |
About Jon Wakefield
Jon Wakefield is a scholar working on Statistics and Probability, Modeling and Simulation and Health, having authored 133 papers that have together received 5.4k indexed citations. Recurring topics across this work include Statistical Methods and Bayesian Inference (39 papers), Statistical Methods in Clinical Trials (17 papers), Data-Driven Disease Surveillance (15 papers), Bayesian Methods and Mixture Models (14 papers), Spatial and Panel Data Analysis (14 papers), Air Quality and Health Impacts (13 papers), Statistical Methods and Inference (11 papers) and COVID-19 epidemiological studies (10 papers). The work is most often cited by research in Statistics and Probability (1.0k citations), Modeling and Simulation (238 citations) and Health, Toxicology and Mutagenesis (574 citations). Jon Wakefield has collaborated with scholars based in United States, United Kingdom and Canada. Frequent co-authors include Paul Elliott, Joshua M. Akey, Serge Aleshin‐Guendel, Victoria Knutson, William Msemburi, Ariel Karlinsky, Stephen Walker, Somnath Chatterji, Gavin Shaddick and Håvard Rue. Their work appears in journals such as Statistics in Medicine, Biometrics, Journal of the Royal Statistical Society Series A (Statistics in Society), The Annals of Applied Statistics 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.