Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Simultaneous analysis of Lasso and Dantzig selector
20091.2k citationsPeter J. Bickel, Ya’acov Ritov et al.profile →
Efficient and Adaptive Estimation for Semiparametric Models.
1994933 citationsPeter J. Bickel, Chris A. J. Klaassen et al.profile →
Efficient and Adaptive Estimation for Semiparametric Models.
1994792 citationsAnton Schίck, Peter J. Bickel et al.Journal of the American Statistical Associationprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
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).
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.
Brown, Lawrence D., Eitan Greenshtein, & Ya’acov Ritov. (2013). The Poisson Compound Decision Problem Revisited. Journal of the American Statistical Association. 108(502). 741–749.15 indexed citations
6.
Zakai, Alon & Ya’acov Ritov. (2009). Consistency and Localizability. Journal of Machine Learning Research. 10(30). 827–856.19 indexed citations
7.
Lach, Saul, Ya’acov Ritov, & Avi Simhon. (2008). The Transmission of Longevity Across Generations. SSRN Electronic Journal.2 indexed citations
8.
Ritov, Ya’acov, et al.. (2008). Semiparametric shift estimation for alignment of ECG data. European Signal Processing Conference. 1–5.1 indexed citations
9.
Bickel, Peter J. & Ya’acov Ritov. (2008). Response to Mease and Wyner, Evidence Contrary to the Statistical View of Boosting, JMLR 9:131-156, 2008: And Yet It Overfits. Journal of Machine Learning Research. 9. 181–186.
10.
Zakai, Alon & Ya’acov Ritov. (2008). How local should a learning method be. Conference on Learning Theory. 205–216.3 indexed citations
11.
Ritov, Ya’acov, et al.. (2008). Semiparametric density estimation of shifts between curves. arXiv (Cornell University).2 indexed citations
12.
Goldberg, Yair, Alon Zakai, Dan Kushnir, & Ya’acov Ritov. (2008). Manifold Learning: The Price of Normalization. Journal of Machine Learning Research. 9(63). 1909–1939.45 indexed citations
Levy, Moshe & Ya’acov Ritov. (2001). Portfolio Optimization with Many Assets: The Importance of Short-Selling. eScholarship (California Digital Library).7 indexed citations
16.
Schίck, Anton, Peter J. Bickel, Chris A. J. Klaassen, Ya’acov Ritov, & Jon A. Wellner. (1994). Efficient and Adaptive Estimation for Semiparametric Models.. Journal of the American Statistical Association. 89(428). 1565–1565.792 indexed citations breakdown →
Bickel, Peter J. & Ya’acov Ritov. (1988). Estimating integrated squared density derivatives. UC Berkeley.14 indexed citations
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.