Charles J. Geyer
- Statistics and Probability top 0.1%
- Artificial Intelligence top 0.5%
- Genetics top 2%
- Molecular Biology
- Ecology, Evolution, Behavior and Systematics top 2%
- Co-authors
- E. A. ThompsonRuth G. ShawJesper MöllerStuart WageniusRobert GentlemanScott D. PletcherFrank H. ShawNed Mohan
- Topics
- Statistical Methods and Inference (28 papers)Statistical Methods and Bayesian Inference (15 papers)Markov Chains and Monte Carlo Methods (14 papers)
- Partner nations
- United StatesFranceRussia
In The Last Decade
Charles J. Geyer
77 papers receiving 5.3k citations
Hit Papers
Peers
Comparison fields: 5 of 187
- Statistics and Probability 1.9k
- Artificial Intelligence 1.5k
- Genetics 993
- Molecular Biology 641
- Ecology, Evolution, Behavior and Systematics 464
Countries citing papers authored by Charles J. Geyer
This map shows the geographic impact of Charles J. Geyer'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 Charles J. Geyer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Charles J. Geyer more than expected).
Fields of papers citing papers by Charles J. Geyer
This network shows the impact of papers produced by Charles J. Geyer. 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 Charles J. Geyer. The network helps show where Charles J. Geyer may publish in the future.
Co-authorship network of co-authors of Charles J. Geyer
This figure shows the co-authorship network connecting the top 25 collaborators of Charles J. Geyer. A scholar is included among the top collaborators of Charles J. Geyer 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 Charles J. Geyer. Charles J. Geyer is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | Aster Models with Random Effects via Penalized Likelihood | 5 |
| 3 | 143 | |
| 4 | A Philosophical Look at Aster Models | 2 |
| 5 | 62 | |
| 6 | 9 | |
| 7 | 162 | |
| 8 | 108 | |
| 9 | 44 | |
| 10 | 2 | |
| 11 | 44 | |
| 12 | 53 | |
| 13 | 68 | |
| 14 | 12 | |
| 15 | 190 | |
| 16 | 3 | |
| 17 | Simulation Procedures and Likelihood Inference for Spatial Point Processes | 231 |
| 18 | 20 | |
| 19 | 54 | |
| 20 | Reweighting Monte Carlo Mixtures | 21 |
About Charles J. Geyer
Charles J. Geyer is a scholar working on Statistics and Probability, Equine and Theoretical Computer Science, having authored 81 papers that have together received 5.6k indexed citations. Recurring topics across this work include Statistical Methods and Inference (28 papers), Statistical Methods and Bayesian Inference (15 papers) and Markov Chains and Monte Carlo Methods (14 papers). The work is most often cited by research in Statistics and Probability (1.9k citations), Artificial Intelligence (1.5k citations) and Equine (74 citations). Charles J. Geyer has collaborated with scholars based in United States, France and Russia. Frequent co-authors include E. A. Thompson, Ruth G. Shaw, Jesper Möller, Stuart Wagenius, Robert Gentleman, Scott D. Pletcher, Frank H. Shaw, Ned Mohan, Julie R. Etterson and Helen H. Hangelbroek. Their work appears in journals such as Journal of the American Statistical Association, The American Naturalist and Genetics.
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.