Ivy Liu

1.6k total citations
51 papers, 839 citations indexed

About

Ivy Liu is a scholar working on Statistics and Probability, Artificial Intelligence and Economics and Econometrics. According to data from OpenAlex, Ivy Liu has authored 51 papers receiving a total of 839 indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Statistics and Probability, 12 papers in Artificial Intelligence and 7 papers in Economics and Econometrics. Recurrent topics in Ivy Liu's work include Statistical Methods and Bayesian Inference (24 papers), Statistical Methods and Inference (9 papers) and Bayesian Methods and Mixture Models (7 papers). Ivy Liu is often cited by papers focused on Statistical Methods and Bayesian Inference (24 papers), Statistical Methods and Inference (9 papers) and Bayesian Methods and Mixture Models (7 papers). Ivy Liu collaborates with scholars based in New Zealand, United States and Taiwan. Ivy Liu's co-authors include Alan Agresti, Daniel Fernández, Bing Xue, Bach Hoai Nguyen, Bhramar Mukherjee, Chih‐Chiang Cheng, Thomas Suesse, Jun Zhang, Mengjie Zhang and Hongtao Yi and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of Applied Physics and Biometrics.

In The Last Decade

Ivy Liu

48 papers receiving 794 citations

Peers

Ivy Liu
Comparison fields: 5 of 144
  • Statistics and Probability 244
  • Artificial Intelligence 136
  • Economics and Econometrics 91
  • Management Science and Operations Research 68
  • Sociology and Political Science 59
Replace Andréas Karlsson with:
Andréas Karlsson Sweden
Jacques Janssen Italy
Alexander Basilevsky Canada
Madelyn Glymour United States
Tapabrata Maiti United States
Nema Dean United Kingdom
Vicente Núñez‐Antón Spain
Javad Behboodian Iran
Jane Galbraith United Kingdom
Andréas Karlsson Sweden View profile →
Citations per field, relative to Ivy Liu
Ivy Liu · 1×
Citations per year, relative to Ivy Liu
Ivy Liu · 1×

Countries citing papers authored by Ivy Liu

Since Specialization
Citations

This map shows the geographic impact of Ivy Liu'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 Ivy Liu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ivy Liu more than expected).

Fields of papers citing papers by Ivy Liu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ivy Liu. 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 Ivy Liu. The network helps show where Ivy Liu may publish in the future.

Co-authorship network of co-authors of Ivy Liu

This figure shows the co-authorship network connecting the top 25 collaborators of Ivy Liu. A scholar is included among the top collaborators of Ivy Liu 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 Ivy Liu. Ivy Liu 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
# Work Indexed citations
1 1
2 1
3 8
4 5
5 0
6 3
7 2
8 33
9 16
10 7
11 43
12 29
13 19
14 2
15 6
16 1
17 15
18 1
19 1
20 37

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.

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