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.
DeepHit: A Deep Learning Approach to Survival Analysis With Competing Risks
2018278 citationsWilliam R. Zame, Jinsung Yoon et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by William R. Zame
Since
Specialization
Citations
This map shows the geographic impact of William R. Zame'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 William R. Zame with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites William R. Zame more than expected).
This network shows the impact of papers produced by William R. Zame. 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 William R. Zame. The network helps show where William R. Zame may publish in the future.
Co-authorship network of co-authors of William R. Zame
This figure shows the co-authorship network connecting the top 25 collaborators of William R. Zame.
A scholar is included among the top collaborators of William R. Zame 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 William R. Zame. William R. Zame is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Qian, Zhaozhi, William R. Zame, Lucas M. Fleuren, Paul Elbers, & Mihaela van der Schaar. (2021). Integrating Expert ODEs into Neural ODEs: Pharmacology and Disease Progression. Pure Amsterdam UMC. 34.1 indexed citations
2.
Crabbé, Jonathan, Yao Zhang, William R. Zame, & Mihaela van der Schaar. (2020). Learning outside the Black-Box: The pursuit of interpretable models. arXiv (Cornell University). 33. 17838–17849.1 indexed citations
3.
Yoon, Jinsung, William R. Zame, & Mihaela van der Schaar. (2018). Deep Sensing: Active Sensing using Multi-directional Recurrent Neural Networks. International Conference on Learning Representations.8 indexed citations
4.
Zame, William R., Jinsung Yoon, Folkert W. Asselbergs, & Mihaela van der Schaar. (2018). Abstract 14882: Interpretable Machine Learning Identifies Risk Predictors in Patients With Heart Failure. Circulation.1 indexed citations
5.
Zame, William R., et al.. (2017). DPSCREEN: Dynamic Personalized Screening. Neural Information Processing Systems. 30. 1321–1332.3 indexed citations
6.
Xu, Jie, William R. Zame, & Mihaela van der Schaar. (2012). Token economy for online exchange systems. Adaptive Agents and Multi-Agents Systems. 1283–1284.1 indexed citations
7.
Zame, William R.. (2007). Can intergenerational equity be operationalized. Theoretical Economics. 2(2).49 indexed citations
8.
Jéhiel, Philippe, Moritz Meyer-ter-Vehn, Benny Moldovanu, & William R. Zame. (2006). The Limits of ex post Implementation. Econometrica. 74(3). 585–610.113 indexed citations
9.
Ellickson, Bryan, Birgit Grodal, Suzanne Scotchmer, & William R. Zame. (2001). . Research at the University of Copenhagen (University of Copenhagen).31 indexed citations
10.
Eckel, Catherine C., Sheryl Ball, Philip J. Grossman, & William R. Zame. (2001). Status in Markets. SSRN Electronic Journal.28 indexed citations
11.
Zame, William R.. (1990). Efficiency and the Role of Default When Security Markets Are Incomplete. American Economic Review. 83(5). 1142–1164.129 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.