Aad van der Vaart
- Statistics and Probability top 0.01%
- Artificial Intelligence top 0.2%
- Finance top 0.5%
- Economics and Econometrics top 1%
- Management Science and Operations Research top 0.5%
- Co-authors
- Jon A. WellnerSusan A. MurphySubhashis GhosalJ. H. van ZantenThomas MikoschJayanta K. GhoshB. J. K. KleijnValérie Ventura
- Topics
- Statistical Methods and Inference (62 papers)Bayesian Methods and Mixture Models (43 papers)Statistical Methods and Bayesian Inference (29 papers)
- Partner nations
- NetherlandsUnited StatesUnited Kingdom
In The Last Decade
Aad van der Vaart
134 papers receiving 10.9k citations
Hit Papers
Peers
Comparison fields: 5 of 196
- Statistics and Probability 7.6k
- Artificial Intelligence 3.5k
- Finance 1.4k
- Economics and Econometrics 1.1k
- Management Science and Operations Research 969
Countries citing papers authored by Aad van der Vaart
This map shows the geographic impact of Aad van der Vaart'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 Aad van der Vaart with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Aad van der Vaart more than expected).
Fields of papers citing papers by Aad van der Vaart
This network shows the impact of papers produced by Aad van der Vaart. 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 Aad van der Vaart. The network helps show where Aad van der Vaart may publish in the future.
Co-authorship network of co-authors of Aad van der Vaart
This figure shows the co-authorship network connecting the top 25 collaborators of Aad van der Vaart. A scholar is included among the top collaborators of Aad van der Vaart 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 Aad van der Vaart. Aad van der Vaart 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 | 22 | |
| 3 | 12 | |
| 4 | How many needles in the haystack? Adaptive inference and uncertainty quantification for the horseshoe | 1 |
| 5 | 4 | |
| 6 | 3 | |
| 7 | 80 | |
| 8 | 69 | |
| 9 | 10 | |
| 10 | 18 | |
| 11 | 10 | |
| 12 | 1 | |
| 13 | Posterior convergence rates of Dirichlet mixtures at smooth densities | 70 |
| 14 | 34 | |
| 15 | 2 | |
| 16 | Groeidiagrammen voor lengte, gewicht en 'body mass index' van tweelingen in de peutertijd | 2 |
| 17 | Finding Clusters using Support Vector Classifiers | 8 |
| 18 | Current Status Regression | 15 |
| 19 | Observed Information in Semiparametric Models | 1 |
| 20 | Statistical estimation in large parameter spaces | 53 |
About Aad van der Vaart
Aad van der Vaart is a scholar working on Statistics and Probability, Artificial Intelligence and Statistics, Probability and Uncertainty, having authored 143 papers that have together received 11.5k indexed citations. Recurring topics across this work include Statistical Methods and Inference (62 papers), Bayesian Methods and Mixture Models (43 papers) and Statistical Methods and Bayesian Inference (29 papers). The work is most often cited by research in Statistics and Probability (7.6k citations), Finance (1.4k citations) and Statistics, Probability and Uncertainty (881 citations). Aad van der Vaart has collaborated with scholars based in Netherlands, United States and United Kingdom. Frequent co-authors include Jon A. Wellner, Susan A. Murphy, Subhashis Ghosal, J. H. van Zanten, Thomas Mikosch, Jayanta K. Ghosh, B. J. K. Kleijn, Valérie Ventura, Mark J. van der Laan and James M. Robins. Their work appears in journals such as Journal of the American Statistical Association, Bioinformatics and NeuroImage.
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.