Hojin Yang
Impact in
- Statistics and Probability top 10%
- Statistical Methods and Inference
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- Cancer survivorship and care
Papers in
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- Statistical Methods and Inference 10
- Statistical Methods and Bayesian Inference 5
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- Topic Modeling 5
- Bayesian Methods and Mixture Models 3
- Sentiment Analysis and Opinion Mining 2
- Co-authors
- Allison M. Deal (8 shared papers)Scott Sanner (4 shared papers)Denise Spector (1 shared paper)Cláudio L. Battaglini (1 shared paper)Keith D. Amos (1 shared paper)Ga Wu (3 shared papers)Kai Luo (2 shared papers)Jeffrey S. Morris (1 shared paper)
- Journals
- Journal of Endourology (1 paper)Journal of the American Statistical Association (1 paper)Integrative Cancer Therapies (1 paper)Frontiers in Oncology (1 paper)Journal of Clinical Oncology (1 paper)
- Partner nations
- United StatesSouth KoreaCanada
In The Last Decade
Hojin Yang
24 papers receiving 277 citations
Peers
Comparison fields: 5 of 82
- Statistics and Probability 40
- Oncology 91
- Artificial Intelligence 69
- Information Systems 47
- Otorhinolaryngology 7
Countries citing papers authored by Hojin Yang
This map shows the geographic impact of Hojin Yang'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 Hojin Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hojin Yang more than expected).
Fields of papers citing papers by Hojin Yang
This network shows the impact of papers produced by Hojin Yang. 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 Hojin Yang. The network helps show where Hojin Yang may publish in the future.
Co-authors
The 25 scholars most cited alongside Hojin Yang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 28 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2013 | 59 | |
| 2 | 2016 | 37 | |
| 3 | 2019 | 28 | |
| 4 | 2020 | 24 | |
| 5 | 2020 | 22 | |
| 6 | 2015 | 17 | |
| 7 | 2016 | 16 | |
| 8 | 2013 | 16 | |
| 9 | 2014 | 12 | |
| 10 | 2019 | 7 | |
| 11 | 2018 | 7 | |
| 12 | 2021 | 7 | |
| 13 | 2018 | 6 | |
| 14 | 2019 | 5 | |
| 15 | 2021 | 5 | |
| 16 | 2021 | 3 | |
| 17 | 2021 | 3 | |
| 18 | 2019 | 3 | |
| 19 | 2023 | 2 | |
| 20 | 2024 | 1 |
About Hojin Yang
Hojin Yang is a scholar working on Statistics and Probability, Artificial Intelligence, Information Systems, Oncology and Surgery, having authored 28 papers that have together received 284 indexed citations. Recurring topics across this work include Statistical Methods and Inference (10 papers), Recommender Systems and Techniques (6 papers), Statistical Methods and Bayesian Inference (5 papers), Topic Modeling (5 papers), Cancer survivorship and care (3 papers), Bayesian Methods and Mixture Models (3 papers), Sentiment Analysis and Opinion Mining (2 papers) and Nasal Surgery and Airway Studies (2 papers). The work is most often cited by research in Statistics and Probability (40 citations), Oncology (91 citations), Artificial Intelligence (69 citations), Information Systems (47 citations) and Otorhinolaryngology (7 citations). Hojin Yang has collaborated with scholars based in United States, South Korea and Canada. Frequent co-authors include Allison M. Deal, Scott Sanner, Denise Spector, Cláudio L. Battaglini, Keith D. Amos, Ga Wu, Kai Luo, Jeffrey S. Morris, Veerabhadran Baladandayuthapani and Arvind Rao. Their work appears in journals such as Journal of Endourology, Journal of the American Statistical Association, Integrative Cancer Therapies, Frontiers in Oncology and Journal of Clinical Oncology.
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.