Daniel Percival
Impact in
- Statistics and Probability top 5%
- Advanced Causal Inference Techniques
- Statistical Methods and Inference
- Statistical Methods and Bayesian Inference
- Accounting top 10%
- Corporate Finance and Governance
- Auditing, Earnings Management, Governance
Papers in
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- Statistical Methods and Inference 4
- Advanced Causal Inference Techniques 1
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- Bioinformatics and Genomic Networks 1
- Co-authors
- Qingyuan Zhao (1 shared paper)Paul S. Moore (1 shared paper)Chris Fraley (1 shared paper)Larry Wasserman (2 shared papers)Roni Rosenfeld (1 shared paper)Di Liu (1 shared paper)Stephen E. Fienberg (1 shared paper)Kathryn Roeder (1 shared paper)
- Journals
- Behaviour (1 paper)Journal of the American Statistical Association (1 paper)Journal of Statistical Computation and Simulation (1 paper)arXiv (Cornell University) (1 paper)Proceedings of the International AAAI Conference on Web and Social Media (1 paper)
- Partner nations
- United States
In The Last Decade
Daniel Percival
6 papers receiving 265 citations
Peers
Comparison fields: 5 of 90
- Statistics and Probability 54
- Accounting 45
- Economics and Econometrics 54
- General Decision Sciences 3
- Finance 16
Countries citing papers authored by Daniel Percival
This map shows the geographic impact of Daniel Percival'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 Daniel Percival with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Percival more than expected).
Fields of papers citing papers by Daniel Percival
This network shows the impact of papers produced by Daniel Percival. 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 Daniel Percival. The network helps show where Daniel Percival may publish in the future.
Co-authors
The 14 scholars most cited alongside Daniel Percival, 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 | 2016 | 247 | |
| 2 | 2010 | 11 | |
| 3 | 2013 | 4 | |
| 4 | 2010 | 4 | |
| 5 | 2014 | 3 | |
| 6 | 2018 | 2 | |
| 7 | Structured sparsity | 2012 | 0 |
About Daniel Percival
Daniel Percival is a scholar working on Statistics and Probability, Molecular Biology, Artificial Intelligence, Ecology, Evolution, Behavior and Systematics and Statistical and Nonlinear Physics, having authored 7 papers that have together received 271 indexed citations. Recurring topics across this work include Statistical Methods and Inference (4 papers), Atmospheric and Environmental Gas Dynamics (1 paper), Advanced Causal Inference Techniques (1 paper), Multi-Criteria Decision Making (1 paper), Bioinformatics and Genomic Networks (1 paper), Cephalopods and Marine Biology (1 paper), earthquake and tectonic studies (1 paper) and Sparse and Compressive Sensing Techniques (1 paper). The work is most often cited by research in Statistics and Probability (54 citations), Accounting (45 citations), Economics and Econometrics (54 citations), General Decision Sciences (3 citations) and Finance (16 citations). Daniel Percival has collaborated with scholars based in United States. Frequent co-authors include Qingyuan Zhao, Paul S. Moore, Chris Fraley, Larry Wasserman, Roni Rosenfeld, Di Liu, Stephen E. Fienberg, Kathryn Roeder, Paul Y. Huang and Harold O. Mofjeld. Their work appears in journals such as Behaviour, Journal of the American Statistical Association, Journal of Statistical Computation and Simulation, arXiv (Cornell University) and Proceedings of the International AAAI Conference on Web and Social Media.
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.