Sara van de Geer
- Statistics and Probability top 0.05%
- Artificial Intelligence top 1%
- Computational Mechanics top 1%
- Computer Vision and Pattern Recognition top 2%
- Molecular Biology
- Co-authors
- Peter BühlmannLukas MeierEnno MammenArie KapteynHuib van de StadtMassimiliano PontilKarim LouniciCun‐Hui Zhang
- Topics
- Statistical Methods and Inference (44 papers)Bayesian Methods and Mixture Models (15 papers)Sparse and Compressive Sensing Techniques (10 papers)
- Journals
- IEEE Transactions on Information TheoryIEEE Transactions on Signal ProcessingThe Review of Economics and Statistics
- Partner nations
- SwitzerlandNetherlandsUnited States
In The Last Decade
Sara van de Geer
60 papers receiving 4.5k citations
Hit Papers
Peers
Comparison fields: 5 of 174
- Statistics and Probability 2.4k
- Artificial Intelligence 1.3k
- Computational Mechanics 873
- Computer Vision and Pattern Recognition 488
- Molecular Biology 475
Countries citing papers authored by Sara van de Geer
This map shows the geographic impact of Sara van de Geer'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 Sara van de Geer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sara van de Geer more than expected).
Fields of papers citing papers by Sara van de Geer
This network shows the impact of papers produced by Sara van de Geer. 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 Sara van de Geer. The network helps show where Sara van de Geer may publish in the future.
Co-authorship network of co-authors of Sara van de Geer
This figure shows the co-authorship network connecting the top 25 collaborators of Sara van de Geer. A scholar is included among the top collaborators of Sara van de Geer 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 Sara van de Geer. Sara van de Geer is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 19 | |
| 3 | 5 | |
| 4 | 21 | |
| 5 | 1 | |
| 6 | 62 | |
| 7 | 5 | |
| 8 | 24 | |
| 9 | 99 | |
| 10 | 56 | |
| 11 | 5 | |
| 12 | Prediction and variable selection with the adaptive Lasso | 4 |
| 13 | 4 | |
| 14 | 2 | |
| 15 | 10 | |
| 16 | 57 | |
| 17 | 15 | |
| 18 | Regression analysis and empirical processes | 12 |
| 19 | 30 | |
| 20 | The Impact of Changes in Income and Family Composition on Subjective Measures of Well-Being | 5 |
About Sara van de Geer
Sara van de Geer is a scholar working on Statistics and Probability, Statistics, Probability and Uncertainty and Artificial Intelligence, having authored 64 papers that have together received 4.7k indexed citations. Recurring topics across this work include Statistical Methods and Inference (44 papers), Bayesian Methods and Mixture Models (15 papers) and Sparse and Compressive Sensing Techniques (10 papers). The work is most often cited by research in Statistics and Probability (2.4k citations), Computational Mechanics (873 citations) and Artificial Intelligence (1.3k citations). Sara van de Geer has collaborated with scholars based in Switzerland, Netherlands and United States. Frequent co-authors include Peter Bühlmann, Lukas Meier, Enno Mammen, Arie Kapteyn, Huib van de Stadt, Massimiliano Pontil, Karim Lounici, Cun‐Hui Zhang, Philipp Rütimann and Mohamed Hebiri. Their work appears in journals such as IEEE Transactions on Information Theory, IEEE Transactions on Signal Processing and The Review of Economics and Statistics.
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.