Shinto Eguchi
Impact in
- Statistics and Probability top 0.5%
- Advanced Statistical Methods and Models
- Statistical Methods and Inference
- Statistical Methods and Bayesian Inference
- Computational Mathematics top 10%
Papers in
-
- Advanced Statistical Methods and Models 27
- Statistical Methods and Inference 24
- Statistical Methods and Bayesian Inference 13
- Co-authors
- J. B. CopasHironori FujisawaMihoko MinamiTakashi TakenouchiOsamu KomoriTakafumi KanamoriNoboru MurataShogo Kato
- Journals
- Neural Computation (10 papers)Journal of Multivariate Analysis (5 papers)Journal of the Royal Statistical Society Series B (Statistical Methodology) (4 papers)BMC Bioinformatics (4 papers)Annals of the Institute of Statistical Mathematics (4 papers)
- Partner nations
- JapanUnited KingdomTaiwan
In The Last Decade
Shinto Eguchi
103 papers receiving 1.8k citations
Peers
Comparison fields: 5 of 159
- Statistics and Probability 734
- Computational Mathematics 17
- Statistical and Nonlinear Physics 284
- Statistics, Probability and Uncertainty 148
- Artificial Intelligence 533
Countries citing papers authored by Shinto Eguchi
This map shows the geographic impact of Shinto Eguchi'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 Shinto Eguchi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shinto Eguchi more than expected).
Fields of papers citing papers by Shinto Eguchi
This network shows the impact of papers produced by Shinto Eguchi. 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 Shinto Eguchi. The network helps show where Shinto Eguchi may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Shinto Eguchi, 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 | 2025 | 1 | |
| 2 | 2025 | 0 | |
| 3 | 2022 | 1 | |
| 4 | 2021 | 7 | |
| 5 | 2020 | 1 | |
| 6 | 2016 | 2 | |
| 7 | 2013 | 1 | |
| 8 | 2013 | 1 | |
| 9 | Boosting for density estimation based on U loss function | 2010 | 1 |
| 10 | 2010 | 4 | |
| 11 | 2008 | 147 | |
| 12 | 2006 | 9 | |
| 13 | 2006 | 29 | |
| 14 | 2005 | 5 | |
| 15 | 2005 | 19 | |
| 16 | 2003 | 13 | |
| 17 | 2003 | 80 | |
| 18 | Recent developments in discriminant analysis from an information geometric point of view | 2001 | 6 |
| 19 | 1990 | 3 | |
| 20 | 1990 | 25 |
About Shinto Eguchi
Shinto Eguchi is a scholar working on Statistics and Probability, Ecological Modeling, Artificial Intelligence, Analytical Chemistry and Statistics, Probability and Uncertainty, having authored 108 papers that have together received 1.9k indexed citations. Recurring topics across this work include Advanced Statistical Methods and Models (27 papers), Statistical Methods and Inference (24 papers), Statistical Methods and Bayesian Inference (13 papers), Bayesian Methods and Mixture Models (12 papers), Gene expression and cancer classification (11 papers), Statistical Mechanics and Entropy (10 papers), Spectroscopy and Chemometric Analyses (10 papers) and Neural Networks and Applications (8 papers). The work is most often cited by research in Statistics and Probability (734 citations), Computational Mathematics (17 citations), Statistical and Nonlinear Physics (284 citations), Statistics, Probability and Uncertainty (148 citations) and Artificial Intelligence (533 citations). Shinto Eguchi has collaborated with scholars based in Japan, United Kingdom and Taiwan. Frequent co-authors include J. B. Copas, Hironori Fujisawa, Mihoko Minami, Takashi Takenouchi, Osamu Komori, Takafumi Kanamori, Noboru Murata, Shogo Kato, Yi-Ren Yeh and Hiroshi Okamura. Their work appears in journals such as Neural Computation, Journal of Multivariate Analysis, Journal of the Royal Statistical Society Series B (Statistical Methodology), BMC Bioinformatics and Annals of the Institute of Statistical Mathematics.
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.