Jacob Bien
Impact in
- Computational Mathematics top 10%
- Statistics and Probability top 2%
- Statistical Methods and Inference
- Advanced Statistical Methods and Models
Papers in
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- Statistical Methods and Inference 14
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- Data Analysis with R 3
- Machine Learning and Algorithms 3
- Co-authors
- Robert Tibshirani (4 shared papers)Ryan J. Tibshirani (3 shared papers)Noah Simon (2 shared papers)Trevor Hastie (1 shared paper)Jerome H. Friedman (1 shared paper)Jonathan Taylor (1 shared paper)Daniela Witten (6 shared papers)Lucy L. Gao (3 shared papers)
- Journals
- Journal of the American Statistical Association (7 papers)The Annals of Applied Statistics (2 papers)Journal of Computational and Graphical Statistics (2 papers)Frontiers in Microbiology (1 paper)Scientific Reports (1 paper)
- Partner nations
- United StatesCanadaGermany
In The Last Decade
Jacob Bien
33 papers receiving 946 citations
Jacob Bien's Hit Papers
Peers
Comparison fields: 5 of 155
- Computational Mathematics 15
- Statistics and Probability 189
- Health Informatics 18
- Artificial Intelligence 316
- Modeling and Simulation 36
Countries citing papers authored by Jacob Bien
This map shows the geographic impact of Jacob Bien'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 Jacob Bien with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jacob Bien more than expected).
Fields of papers citing papers by Jacob Bien
This network shows the impact of papers produced by Jacob Bien. 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 Jacob Bien. The network helps show where Jacob Bien may publish in the future.
Co-authors
The 25 scholars most cited alongside Jacob Bien, 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 35 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Strong Rules for Discarding Predictors in Lasso-Type Problems Hit paper breakdown → | 2011 | 393 |
| 2 | 2011 | 132 | |
| 3 | 2011 | 95 | |
| 4 | 2022 | 50 | |
| 5 | 2015 | 30 | |
| 6 | 2011 | 27 | |
| 7 | 2015 | 26 | |
| 8 | 2017 | 25 | |
| 9 | 2021 | 22 | |
| 10 | 2022 | 21 | |
| 11 | 2018 | 21 | |
| 12 | 2019 | 21 | |
| 13 | 2020 | 19 | |
| 14 | 2010 | 19 | |
| 15 | 2021 | 13 | |
| 16 | 2019 | 13 | |
| 17 | 2022 | 6 | |
| 18 | 1994 | 6 | |
| 19 | 2024 | 6 | |
| 20 | Learning local dependence in ordered data | 2017 | 5 |
About Jacob Bien
Jacob Bien is a scholar working on Statistics and Probability, Artificial Intelligence, Molecular Biology, Oceanography and Computer Vision and Pattern Recognition, having authored 35 papers that have together received 976 indexed citations. Recurring topics across this work include Statistical Methods and Inference (14 papers), Sparse and Compressive Sensing Techniques (4 papers), Gene expression and cancer classification (4 papers), Face and Expression Recognition (3 papers), Marine and coastal ecosystems (3 papers), Data Analysis with R (3 papers), Machine Learning and Algorithms (3 papers) and Oceanographic and Atmospheric Processes (3 papers). The work is most often cited by research in Computational Mathematics (15 citations), Statistics and Probability (189 citations), Health Informatics (18 citations), Artificial Intelligence (316 citations) and Modeling and Simulation (36 citations). Jacob Bien has collaborated with scholars based in United States, Canada and Germany. Frequent co-authors include Robert Tibshirani, Ryan J. Tibshirani, Noah Simon, Trevor Hastie, Jerome H. Friedman, Jonathan Taylor, Daniela Witten, Lucy L. Gao, Shuxiao Chen and Luo Xiao. Their work appears in journals such as Journal of the American Statistical Association, The Annals of Applied Statistics, Journal of Computational and Graphical Statistics, Frontiers in Microbiology and Scientific Reports.
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.