Ya’acov Ritov
- Statistics and Probability top 0.05%
- Artificial Intelligence top 1%
- Computational Mechanics top 2%
- Economics and Econometrics top 2%
- Finance top 2%
- Co-authors
- Peter J. BickelAlexandre B. TsybakovJon A. WellnerChris A. J. KlaassenAnton SchίckJames M. RobinsEitan GreenshteinHagai Bergman
- Topics
- Statistical Methods and Inference (52 papers)Statistical Methods and Bayesian Inference (20 papers)Bayesian Methods and Mixture Models (17 papers)
- Journals
- Proceedings of the National Academy of SciencesJournal of NeuroscienceJournal of the American Statistical Association
- Partner nations
- IsraelUnited StatesAustralia
In The Last Decade
Ya’acov Ritov
110 papers receiving 5.4k citations
Hit Papers
Peers
Comparison fields: 5 of 187
- Statistics and Probability 3.4k
- Artificial Intelligence 1.4k
- Computational Mechanics 574
- Economics and Econometrics 457
- Finance 433
Countries citing papers authored by Ya’acov Ritov
This map shows the geographic impact of Ya’acov Ritov'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 Ya’acov Ritov with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ya’acov Ritov more than expected).
Fields of papers citing papers by Ya’acov Ritov
This network shows the impact of papers produced by Ya’acov Ritov. 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 Ya’acov Ritov. The network helps show where Ya’acov Ritov may publish in the future.
Co-authorship network of co-authors of Ya’acov Ritov
This figure shows the co-authorship network connecting the top 25 collaborators of Ya’acov Ritov. A scholar is included among the top collaborators of Ya’acov Ritov 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 Ya’acov Ritov. Ya’acov Ritov is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Inference In General Single-Index Models Under High-dimensional Symmetric Designs | 0 |
| 2 | 10 | |
| 3 | 3 | |
| 4 | 15 | |
| 5 | 29 | |
| 6 | Consistency and Localizability | 19 |
| 7 | Semiparametric shift estimation for alignment of ECG data | 1 |
| 8 | The Transmission of Longevity Across Generations | 2 |
| 9 | Semiparametric density estimation of shifts between curves | 2 |
| 10 | How local should a learning method be | 3 |
| 11 | Response to Mease and Wyner, Evidence Contrary to the Statistical View of Boosting, JMLR 9:131-156, 2008: And Yet It Overfits | 0 |
| 12 | 32 | |
| 13 | 72 | |
| 14 | 2 | |
| 15 | Portfolio Optimization with Many Assets: The Importance of Short-Selling | 7 |
| 16 | 79 | |
| 17 | 36 | |
| 18 | 6 | |
| 19 | 17 | |
| 20 | 1 |
About Ya’acov Ritov
Ya’acov Ritov is a scholar working on Statistics and Probability, Statistics, Probability and Uncertainty and Medical Laboratory Technology, having authored 118 papers that have together received 5.9k indexed citations. Recurring topics across this work include Statistical Methods and Inference (52 papers), Statistical Methods and Bayesian Inference (20 papers) and Bayesian Methods and Mixture Models (17 papers). The work is most often cited by research in Statistics and Probability (3.4k citations), Statistics, Probability and Uncertainty (365 citations) and Finance (433 citations). Ya’acov Ritov has collaborated with scholars based in Israel, United States and Australia. Frequent co-authors include Peter J. Bickel, Alexandre B. Tsybakov, Jon A. Wellner, Chris A. J. Klaassen, Anton Schίck, James M. Robins, Eitan Greenshtein, Hagai Bergman, Izhar Bar‐Gad and Tobias Rydén. Their work appears in journals such as Proceedings of the National Academy of Sciences, Journal of Neuroscience and Journal of the American Statistical Association.
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.