Robert Kohn
- Statistics and Probability top 0.1%
- Statistical Methods and Inference 70
- Statistical Methods and Bayesian Inference 52
- Advanced Statistical Methods and Models 30
- Markov Chains and Monte Carlo Methods 23
- Finance top 0.5%
- Financial Risk and Volatility Modeling 33
- Economics and Econometrics top 0.5%
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- Bayesian Methods and Mixture Models 58
- Target Tracking and Data Fusion in Sensor Networks 15
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- Control Systems and Identification 13
Robert Kohn
207 papers receiving 5.7k citations
Hit Papers
Peers
Comparison fields: 5 of 195
- Statistics and Probability 2.3k
- General Economics, Econometrics and Finance 1.1k
- Finance 1.3k
- Management Science and Operations Research 784
- Economics and Econometrics 1.7k
Countries citing papers authored by Robert Kohn
This map shows the geographic impact of Robert Kohn'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 Robert Kohn with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Robert Kohn more than expected).
Fields of papers citing papers by Robert Kohn
This network shows the impact of papers produced by Robert Kohn. 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 Robert Kohn. The network helps show where Robert Kohn may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Robert Kohn, 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 | 2023 | 3 | |
| 2 | 2022 | 2 | |
| 3 | 2020 | 3 | |
| 4 | On some variance reduction properties of the reparameterization trick. | 2018 | 1 |
| 5 | Subsampling MCMC - A review for the survey statistician | 2018 | 1 |
| 6 | The Approximation Properties of Copulas by Mixtures | 2017 | 1 |
| 7 | Flexible Particle Markov chain Monte Carlo methods with an application to a factor stochastic volatility model | 2014 | 0 |
| 8 | On Feynman-Kac and particle Markov chain Monte Carlo models | 2014 | 1 |
| 9 | On general sampling schemes for Particle Markov chain Monte Carlo methods | 2014 | 2 |
| 10 | 2013 | 4 | |
| 11 | 2012 | 6 | |
| 12 | On Some Properties of Markov Chain Monte Carlo Simulation Methods Based on the Particle Filter | 2012 | 4 |
| 13 | Regression Density Estimation Using Smooth Adaptive Gaussian Mixtures | 2007 | 2 |
| 14 | 2000 | 11 | |
| 15 | 1999 | 63 | |
| 16 | 1998 | 39 | |
| 17 | 1987 | 3 | |
| 18 | 1986 | 21 | |
| 19 | 1986 | 151 | |
| 20 | Effect of nutrition on cholesterol and phospholipid levels in infants. | 1959 | 0 |
About Robert Kohn
Robert Kohn is a scholar working on Statistics and Probability, Finance and Artificial Intelligence, having authored 218 papers that have together received 6.2k indexed citations. Recurring topics across this work include Statistical Methods and Inference (70 papers), Bayesian Methods and Mixture Models (58 papers), Statistical Methods and Bayesian Inference (52 papers), Financial Risk and Volatility Modeling (33 papers), Advanced Statistical Methods and Models (30 papers), Markov Chains and Monte Carlo Methods (23 papers), Target Tracking and Data Fusion in Sensor Networks (15 papers) and Control Systems and Identification (13 papers). The work is most often cited by research in Statistics and Probability (2.3k citations), General Economics, Econometrics and Finance (1.1k citations) and Finance (1.3k citations). Robert Kohn has collaborated with scholars based in Australia, United States and Sweden. Frequent co-authors include Chris Carter, Craig F. Ansley, Michael S. Smith, M. Pitt, Paolo Giordani, David Chan, David J. Nott, Sally Wood, Mattias Villani and Thomas S. Shively. Their work appears in journals such as Biometrika, Journal of the American Statistical Association, Journal of Econometrics, Journal of Computational and Graphical Statistics and Journal of Statistical Computation and Simulation.
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.