George R. Terrell
- Statistics and Probability top 0.5%
- Artificial Intelligence top 2%
- Control and Systems Engineering top 5%
- Computer Vision and Pattern Recognition top 5%
- Statistics, Probability and Uncertainty top 2%
- Co-authors
- David W. ScottJames Q. SmithRobert M. OliverInyoung KimS. Rao JammalamadakaSteven J. KathmanJinsong ChenLei Liu
- Topics
- Statistical Methods and Inference (12 papers)Bayesian Methods and Mixture Models (7 papers)Advanced Statistical Methods and Models (6 papers)
- Partner nations
- United StatesSwitzerlandCanada
In The Last Decade
George R. Terrell
23 papers receiving 1.4k citations
Hit Papers
Peers
Comparison fields: 5 of 148
- Statistics and Probability 631
- Artificial Intelligence 503
- Control and Systems Engineering 213
- Computer Vision and Pattern Recognition 184
- Statistics, Probability and Uncertainty 141
Countries citing papers authored by George R. Terrell
This map shows the geographic impact of George R. Terrell'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 George R. Terrell with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites George R. Terrell more than expected).
Fields of papers citing papers by George R. Terrell
This network shows the impact of papers produced by George R. Terrell. 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 George R. Terrell. The network helps show where George R. Terrell may publish in the future.
Co-authorship network of co-authors of George R. Terrell
This figure shows the co-authorship network connecting the top 25 collaborators of George R. Terrell. A scholar is included among the top collaborators of George R. Terrell 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 George R. Terrell. George R. Terrell is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 5 | |
| 2 | 0 | |
| 3 | 12 | |
| 4 | 0 | |
| 5 | 2 | |
| 6 | 2 | |
| 7 | 7 | |
| 8 | 4 | |
| 9 | 27 | |
| 10 | 20 | |
| 11 | 12 | |
| 12 | Variable Kernel Density Estimationbreakdown → | 568 |
| 13 | 3 | |
| 14 | 201 | |
| 15 | 313 | |
| 16 | 64 | |
| 17 | 139 | |
| 18 | 20 | |
| 19 | 25 | |
| 20 | 60 |
About George R. Terrell
George R. Terrell is a scholar working on Statistics and Probability, Artificial Intelligence and Mathematical Physics, having authored 26 papers that have together received 1.5k indexed citations. Recurring topics across this work include Statistical Methods and Inference (12 papers), Bayesian Methods and Mixture Models (7 papers) and Advanced Statistical Methods and Models (6 papers). The work is most often cited by research in Statistics and Probability (631 citations), Statistics, Probability and Uncertainty (141 citations) and Artificial Intelligence (503 citations). George R. Terrell has collaborated with scholars based in United States, Switzerland and Canada. Frequent co-authors include David W. Scott, James Q. Smith, Robert M. Oliver, Inyoung Kim, S. Rao Jammalamadaka, Steven J. Kathman, Jinsong Chen, Lei Liu, Martha L. Daviglus and Jinsong Chen. Their work appears in journals such as Journal of the American Statistical Association, Technometrics and Biometrika.
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.