J. Martı́n
- Artificial Intelligence top 5%
- Physiology
- Statistics and Probability top 2%
- Signal Processing top 5%
- Statistics, Probability and Uncertainty top 2%
- Co-authors
- Carlos J. PérezLizbeth NaranjoY. Campos‐RocaDavid Rı́os InsuaVictor S. Martı́nManuel NorteFabrizio RuggeriAlfonso Suárez‐Llorens
- Topics
- Statistical Methods and Bayesian Inference (18 papers)Bayesian Methods and Mixture Models (15 papers)Statistical Distribution Estimation and Applications (12 papers)
In The Last Decade
J. Martı́n
57 papers receiving 681 citations
Peers
Comparison fields: 5 of 119
- Artificial Intelligence 208
- Physiology 183
- Statistics and Probability 169
- Signal Processing 112
- Statistics, Probability and Uncertainty 102
Countries citing papers authored by J. Martı́n
This map shows the geographic impact of J. Martı́n'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 J. Martı́n with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites J. Martı́n more than expected).
Fields of papers citing papers by J. Martı́n
This network shows the impact of papers produced by J. Martı́n. 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 J. Martı́n. The network helps show where J. Martı́n may publish in the future.
Co-authorship network of co-authors of J. Martı́n
This figure shows the co-authorship network connecting the top 25 collaborators of J. Martı́n. A scholar is included among the top collaborators of J. Martı́n 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 J. Martı́n. J. Martı́n 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 | 10 | |
| 3 | 5 | |
| 4 | 80 | |
| 5 | 24 | |
| 6 | 2 | |
| 7 | 8 | |
| 8 | 4 | |
| 9 | 9 | |
| 10 | 7 | |
| 11 | 16 | |
| 12 | 3 | |
| 13 | Misclassified multinomial data: a Bayesian approach | 12 |
| 14 | 7 | |
| 15 | Bayesian Robustness with Quantile Loss Functions. | 4 |
| 16 | 6 | |
| 17 | Approximate Solutions of Complex Influence Diagrams through MCMC Methods. | 6 |
| 18 | 4 | |
| 19 | 1 | |
| 20 | 28 |
About J. Martı́n
J. Martı́n is a scholar working on Statistics and Probability, Statistics, Probability and Uncertainty and General Decision Sciences, having authored 57 papers that have together received 727 indexed citations. Recurring topics across this work include Statistical Methods and Bayesian Inference (18 papers), Bayesian Methods and Mixture Models (15 papers) and Statistical Distribution Estimation and Applications (12 papers). The work is most often cited by research in Statistics and Probability (169 citations), Statistics, Probability and Uncertainty (102 citations) and Signal Processing (112 citations). J. Martı́n has collaborated with scholars based in Spain, Mexico and Italy. Frequent co-authors include Carlos J. Pérez, Lizbeth Naranjo, Y. Campos‐Roca, David Rı́os Insua, Victor S. Martı́n, Manuel Norte, Fabrizio Ruggeri, Alfonso Suárez‐Llorens, Concha Bielza and Steven Drexler. Their work appears in journals such as European Journal of Operational Research, Expert Systems with Applications and Tetrahedron.
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.