Peng Lai

626 citations
51 papers · 400 · h-index 12

Impact in

Papers in

    • Statistical Methods and Inference 31
    • Statistical Methods and Bayesian Inference 17
    • Advanced Statistical Methods and Models 12
    • Advanced Causal Inference Techniques 3
    • Bayesian Methods and Mixture Models 10

Peng Lai

44 papers receiving 391 citations

Peers

Peng Lai
Comparison fields: 5 of 98
  • Statistics and Probability 209
  • Geriatrics and Gerontology 12
  • Artificial Intelligence 72
  • Finance 14
  • Biochemistry 8
Replace Lianming Wang with:
Lianming Wang United States
Faisal Maqbool Zahid Pakistan
Xiaohong Qi China
Wenlin Dai China
Wenkai Zhang China
Xin Xing United States
Sabrina Giordano Italy
Peng-Liang Zhao United States
Konstantinos Adamidis Greece
Ana María Martínez-Rodríguez Spain
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Citations per field
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Lianming Wang · 1×
Citations per year

Countries citing papers authored by Peng Lai

Since Specialization
Citations

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

Fields of papers citing papers by Peng Lai

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Peng Lai, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Peng Lai Line = papers co-authored together Peng Lai links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 51 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201434
2 201133
3 201128
4 201627
5 202222
6 201922
7 202021
8 201320
9 201416
10 201812
11 201311
12 201411
13 201911
14 201410
15
Susceptibility of marmosets to Epstein-Barr virus-like baboon herpesviruses.
197810
16 20179
17 20169
18 20258
19 20138
20 20178

About Peng Lai

Peng Lai is a scholar working on Statistics and Probability, Artificial Intelligence, Molecular Biology, Economics and Econometrics and Genetics, having authored 51 papers that have together received 400 indexed citations. Recurring topics across this work include Statistical Methods and Inference (31 papers), Statistical Methods and Bayesian Inference (17 papers), Advanced Statistical Methods and Models (12 papers), Bayesian Methods and Mixture Models (10 papers), Genetic and phenotypic traits in livestock (4 papers), Spatial and Panel Data Analysis (4 papers), Advanced Causal Inference Techniques (3 papers) and Cancer-related molecular mechanisms research (2 papers). The work is most often cited by research in Statistics and Probability (209 citations), Geriatrics and Gerontology (12 citations), Artificial Intelligence (72 citations), Finance (14 citations) and Biochemistry (8 citations). Peng Lai has collaborated with scholars based in China, Singapore and United States. Frequent co-authors include Heng Lian, Qihua Wang, Gaorong Li, Yixin Liu, Zhi Liu, Yixin Liu, Ya-Shu Chen, Yongjie Li, Yao He and Xi Kuang. Their work appears in journals such as Computational Statistics & Data Analysis, Journal of Multivariate Analysis, Journal of Statistical Computation and Simulation, Statistics and Computing and Journal of Statistical Planning and Inference.

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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