Jie Peng

1.6k citations
56 papers · 994 indexed · h-index 14
Topics
Statistical Methods and Bayesian Inference (7 papers)Advanced Numerical Methods in Computational Mathematics (7 papers)Statistical Methods and Inference (6 papers)

In The Last Decade

Jie Peng

49 papers receiving 970 citations

Peers

Jie Peng
Comparison fields: 5 of 150
  • Genetics 400
  • Statistics and Probability 156
  • Molecular Biology 150
  • Artificial Intelligence 124
  • Civil and Structural Engineering 58
Replace Xianyang Zhang with:
Xianyang Zhang United States
Carl M. O’Brien United Kingdom
Ivo Alberink Netherlands
Mikael Sunnåker Switzerland
Torben Schulz‐Streeck Germany
Robert G. Aykroyd United Kingdom
Christian Lavergne France
Sungjin Ahn South Korea
Juho Piironen Finland
Jie Peng relative to Xianyang Zhang United States Xianyang Zhang's profile →
Citations per field
00.5×8.3×
Xianyang Zhang · 1×
Citations per year

Countries citing papers authored by Jie Peng

Since Specialization
Citations

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

Fields of papers citing papers by Jie Peng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jie Peng

This figure shows the co-authorship network connecting the top 25 collaborators of Jie Peng. A scholar is included among the top collaborators of Jie Peng 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 Jie Peng. Jie Peng 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
#WorkIndexed citations
1 0
2 12
3 0
4 0
5 15
6 4
7 0
8 2
9 6
10 14
11 4
12 11
13 1
14 2
15 20
16
Bootstrap Inference for Network Construction
9
17 22
18 6
19 440
20 6

About Jie Peng

Jie Peng is a scholar working on Statistics and Probability, Computational Mechanics and Numerical Analysis, having authored 56 papers that have together received 994 indexed citations. Recurring topics across this work include Statistical Methods and Bayesian Inference (7 papers), Advanced Numerical Methods in Computational Mathematics (7 papers) and Statistical Methods and Inference (6 papers). The work is most often cited by research in Statistics and Probability (156 citations), Genetics (400 citations) and Artificial Intelligence (124 citations). Jie Peng has collaborated with scholars based in China, United States and Australia. Frequent co-authors include Pei Wang, Neil Risch, Hua Tang, Debashis Paul, Hans‐Georg Müller, K. Krishnamoorthy, David Siegmund, Bing Li, Xiusong Shi and Yongfeng Deng. Their work appears in journals such as Proceedings of the National Academy of Sciences, SHILAP Revista de lepidopterología 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.

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