Makoto Aoshima
- Statistics and Probability top 1%
- Artificial Intelligence top 5%
- Molecular Biology
- Computer Vision and Pattern Recognition top 10%
- Signal Processing top 10%
- Co-authors
- Kazuyoshi YataNitis MukhopadhyayYoshikazu TakadaDan ShenEdward J. DudewiczYi‐Hui ZhouJ. S. MarronHaipeng Shen
- Topics
- Statistical Methods and Inference (33 papers)Bayesian Methods and Mixture Models (20 papers)Gene expression and cancer classification (17 papers)
- Journals
- The Astrophysical Journal Supplement SeriesJournal of Multivariate AnalysisJournal of Statistical Planning and Inference
- Partner nations
- JapanUnited StatesCzechia
In The Last Decade
Makoto Aoshima
63 papers receiving 646 citations
Peers
Comparison fields: 5 of 69
- Statistics and Probability 471
- Artificial Intelligence 269
- Molecular Biology 160
- Computer Vision and Pattern Recognition 105
- Signal Processing 60
Countries citing papers authored by Makoto Aoshima
This map shows the geographic impact of Makoto Aoshima'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 Makoto Aoshima with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Makoto Aoshima more than expected).
Fields of papers citing papers by Makoto Aoshima
This network shows the impact of papers produced by Makoto Aoshima. 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 Makoto Aoshima. The network helps show where Makoto Aoshima may publish in the future.
Co-authorship network of co-authors of Makoto Aoshima
This figure shows the co-authorship network connecting the top 25 collaborators of Makoto Aoshima. A scholar is included among the top collaborators of Makoto Aoshima 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 Makoto Aoshima. Makoto Aoshima is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 5 | |
| 3 | 0 | |
| 4 | 16 | |
| 5 | 16 | |
| 6 | 18 | |
| 7 | 17 | |
| 8 | 34 | |
| 9 | 6 | |
| 10 | 77 | |
| 11 | 44 | |
| 12 | 4 | |
| 13 | 2 | |
| 14 | 7 | |
| 15 | 5 | |
| 16 | 2 | |
| 17 | 11 | |
| 18 | 8 | |
| 19 | 1 | |
| 20 | 8 |
About Makoto Aoshima
Makoto Aoshima is a scholar working on Statistics and Probability, Statistics, Probability and Uncertainty and Management Science and Operations Research, having authored 67 papers that have together received 663 indexed citations. Recurring topics across this work include Statistical Methods and Inference (33 papers), Bayesian Methods and Mixture Models (20 papers) and Gene expression and cancer classification (17 papers). The work is most often cited by research in Statistics and Probability (471 citations), Artificial Intelligence (269 citations) and Statistics, Probability and Uncertainty (60 citations). Makoto Aoshima has collaborated with scholars based in Japan, United States and Czechia. Frequent co-authors include Kazuyoshi Yata, Nitis Mukhopadhyay, Yoshikazu Takada, Dan Shen, Edward J. Dudewicz, Yi‐Hui Zhou, J. S. Marron, Haipeng Shen, Muni S. Srivastava and Yuko Kobayashi. Their work appears in journals such as The Astrophysical Journal Supplement Series, Journal of Multivariate Analysis and Journal of Statistical Planning and Inference.
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.