James P. Hobert
- Statistics and Probability top 0.2%
- Artificial Intelligence top 1%
- Economics and Econometrics top 5%
- Management Science and Operations Research top 5%
- Genetics
- Co-authors
- James G. BoothGeorge CasellaGalin L. JonesVivekananda RoyKshitij KhareAlan AgrestiBrian CaffoChristian P. Robert
- Topics
- Markov Chains and Monte Carlo Methods (43 papers)Bayesian Methods and Mixture Models (41 papers)Statistical Methods and Inference (37 papers)
- Cited by
- Statistics and ProbabilityArtificial IntelligenceManagement Science and Operations Research
- Journals
- Journal of the American Statistical AssociationBiometrikaJournal of the Royal Statistical Society Series B (Statistical Methodology)
- Partner nations
- United StatesFranceAustralia
In The Last Decade
James P. Hobert
66 papers receiving 2.0k citations
Peers
Comparison fields: 5 of 154
- Statistics and Probability 1.5k
- Artificial Intelligence 912
- Economics and Econometrics 213
- Management Science and Operations Research 196
- Genetics 142
Countries citing papers authored by James P. Hobert
This map shows the geographic impact of James P. Hobert'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 James P. Hobert with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites James P. Hobert more than expected).
Fields of papers citing papers by James P. Hobert
This network shows the impact of papers produced by James P. Hobert. 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 James P. Hobert. The network helps show where James P. Hobert may publish in the future.
Co-authorship network of co-authors of James P. Hobert
This figure shows the co-authorship network connecting the top 25 collaborators of James P. Hobert. A scholar is included among the top collaborators of James P. Hobert 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 James P. Hobert. James P. Hobert is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | Wasserstein-based methods for convergence complexity analysis of MCMC with application to Albert and Chib's algorithm | 2 |
| 3 | 5 | |
| 4 | 1 | |
| 5 | 7 | |
| 6 | Moralizing perfect sampling | 0 |
| 7 | 14 | |
| 8 | 23 | |
| 9 | 16 | |
| 10 | 3 | |
| 11 | 62 | |
| 12 | 2 | |
| 13 | 1 | |
| 14 | 3 | |
| 15 | 7 | |
| 16 | 38 | |
| 17 | 44 | |
| 18 | 25 | |
| 19 | 293 | |
| 20 | Spatial Analysis of the Fish Species Richness of Adirondack Lakes: Applications of Geostatistics and Nonparametric Regression | 1 |
About James P. Hobert
James P. Hobert is a scholar working on Statistics and Probability, Artificial Intelligence and Mathematical Physics, having authored 68 papers that have together received 2.1k indexed citations. Recurring topics across this work include Markov Chains and Monte Carlo Methods (43 papers), Bayesian Methods and Mixture Models (41 papers) and Statistical Methods and Inference (37 papers). The work is most often cited by research in Statistics and Probability (1.5k citations), Artificial Intelligence (912 citations) and Management Science and Operations Research (196 citations). James P. Hobert has collaborated with scholars based in United States, France and Australia. Frequent co-authors include James G. Booth, George Casella, Galin L. Jones, James G. Booth, Vivekananda Roy, Kshitij Khare, Alan Agresti, Brian Caffo, Christian P. Robert and Herwig Friedl. Their work appears in journals such as Journal of the American Statistical Association, Biometrika and Journal of the Royal Statistical Society Series B (Statistical Methodology).
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.