Ronald C. Neath
Impact in
- Statistics and Probability top 2%
- Statistical Methods and Inference
- Markov Chains and Monte Carlo Methods
- Statistical Methods and Bayesian Inference
- Artificial Intelligence top 10%
- Bayesian Methods and Mixture Models
- Gaussian Processes and Bayesian Inference
Papers in
-
- Statistical Methods and Inference 4
- Markov Chains and Monte Carlo Methods 4
-
- Bayesian Methods and Mixture Models 3
- Co-authors
- Galin L. Jones (4 shared papers)Murali Haran (1 shared paper)Brian Caffo (1 shared paper)James M. Flegal (1 shared paper)Alicia A. Johnson (1 shared paper)
- Journals
- Electronic Journal of Statistics (1 paper)Statistical Science (1 paper)Journal of the American Statistical Association (1 paper)International Real Estate Review (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- United States
In The Last Decade
Ronald C. Neath
4 papers receiving 206 citations
Peers
Comparison fields: 5 of 73
- Statistics and Probability 151
- Artificial Intelligence 99
- Statistics, Probability and Uncertainty 16
- Mathematical Physics 14
- Environmental Engineering 15
Countries citing papers authored by Ronald C. Neath
This map shows the geographic impact of Ronald C. Neath'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 Ronald C. Neath with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ronald C. Neath more than expected).
Fields of papers citing papers by Ronald C. Neath
This network shows the impact of papers produced by Ronald C. Neath. 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 Ronald C. Neath. The network helps show where Ronald C. Neath may publish in the future.
Co-authors
The 5 scholars most cited alongside Ronald C. Neath, 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 | 2006 | 198 | |
| 2 | 2014 | 18 | |
| 3 | Component-wise Markov chain Monte Carlo | 2009 | 4 |
| 4 | Variable-at-a-time Implementations of Metropolis-Hastings | 2009 | 3 |
| 5 | 2016 | 0 |
About Ronald C. Neath
Ronald C. Neath is a scholar working on Statistics and Probability, Artificial Intelligence, Control and Systems Engineering, Law and Economics and Econometrics, having authored 5 papers that have together received 223 indexed citations. Recurring topics across this work include Statistical Methods and Inference (4 papers), Markov Chains and Monte Carlo Methods (4 papers), Bayesian Methods and Mixture Models (3 papers), Housing Market and Economics (1 paper), Property Rights and Legal Doctrine (1 paper), Financial Literacy, Pension, Retirement Analysis (1 paper) and Control Systems and Identification (1 paper). The work is most often cited by research in Statistics and Probability (151 citations), Artificial Intelligence (99 citations), Statistics, Probability and Uncertainty (16 citations), Mathematical Physics (14 citations) and Environmental Engineering (15 citations). Ronald C. Neath has collaborated with scholars based in United States. Frequent co-authors include Galin L. Jones, Murali Haran, Brian Caffo, James M. Flegal and Alicia A. Johnson. Their work appears in journals such as Electronic Journal of Statistics, Statistical Science, Journal of the American Statistical Association, International Real Estate Review and arXiv (Cornell University).
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.