Peisong Han

1.2k total citations
53 papers, 703 citations indexed

About

Peisong Han is a scholar working on Statistics and Probability, Artificial Intelligence and Economics and Econometrics. According to data from OpenAlex, Peisong Han has authored 53 papers receiving a total of 703 indexed citations (citations by other indexed papers that have themselves been cited), including 37 papers in Statistics and Probability, 6 papers in Artificial Intelligence and 5 papers in Economics and Econometrics. Recurrent topics in Peisong Han's work include Statistical Methods and Inference (35 papers), Statistical Methods and Bayesian Inference (33 papers) and Advanced Causal Inference Techniques (19 papers). Peisong Han is often cited by papers focused on Statistical Methods and Inference (35 papers), Statistical Methods and Bayesian Inference (33 papers) and Advanced Causal Inference Techniques (19 papers). Peisong Han collaborates with scholars based in United States, Canada and China. Peisong Han's co-authors include Lianzhou Wang, Jerald F. Lawless, Melvin G. McInnis, Anastasia K. Yocum, Peter X.‐K. Song, Jeremy M. G. Taylor, Benjamin M. Segal, Ashok Srinivasan, Amanda K. Huber and Xu Zhang and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of the American Statistical Association and PLoS ONE.

In The Last Decade

Peisong Han

45 papers receiving 661 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Peisong Han United States 14 466 67 55 48 36 53 703
Leilei Zeng Canada 12 118 0.3× 25 0.4× 44 0.8× 13 0.3× 14 0.4× 38 443
Seong-Ju Kim South Korea 11 65 0.1× 13 0.2× 42 0.8× 69 1.4× 12 0.3× 38 456
Giusi Moffa Switzerland 12 31 0.1× 54 0.8× 13 0.2× 41 0.9× 100 2.8× 34 575
Nicolás Ballarini Austria 10 66 0.1× 16 0.2× 36 0.7× 16 0.3× 10 0.3× 22 272
Diana Kelmansky Argentina 10 43 0.1× 38 0.6× 10 0.2× 23 0.5× 13 0.4× 25 309
Lamiae Azizi Australia 11 35 0.1× 58 0.9× 29 0.5× 22 0.5× 17 0.5× 20 454
Amelie Elsäßer Germany 11 61 0.1× 4 0.1× 52 0.9× 36 0.8× 12 0.3× 17 382
Moritz Berger Germany 14 71 0.2× 37 0.6× 24 0.4× 7 0.1× 21 0.6× 54 467
Maria Sudell United Kingdom 7 46 0.1× 14 0.2× 26 0.5× 13 0.3× 8 0.2× 12 427
Alan Maloney United States 13 97 0.2× 7 0.1× 17 0.3× 7 0.1× 20 0.6× 35 575

Countries citing papers authored by Peisong Han

Since Specialization
Citations

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

Fields of papers citing papers by Peisong Han

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Peisong Han

This figure shows the co-authorship network connecting the top 25 collaborators of Peisong Han. A scholar is included among the top collaborators of Peisong Han 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 Peisong Han. Peisong Han 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.
Zhang, Weitao, et al.. (2025). Improvement strategies and research progress of silicon/graphite composites in lithium-ion batteries. FlatChem. 50. 100833–100833. 9 indexed citations
2.
Carlozzi, Noelle E., et al.. (2025). Preliminary Testing of the Discussion of Patient Life Goals Patient-Reported Outcome Measure for Dialysis Facilities. Kidney Medicine. 7(4). 100972–100972.
3.
Ryan, Kelly A., Anastasia K. Yocum, Peisong Han, et al.. (2025). Predictive evidence for the impact of personality styles on depression and functioning in two bipolar disorder cohorts. Journal of Affective Disorders. 380. 746–755.
4.
Han, Peisong, et al.. (2024). Integrating external summary information under population heterogeneity and information uncertainty. Electronic Journal of Statistics. 18(2).
5.
Han, Peisong, et al.. (2024). Exploring the Big Data Paradox for various estimands using vaccination data from the global COVID-19 Trends and Impact Survey (CTIS). Science Advances. 10(22). eadj0266–eadj0266. 2 indexed citations
6.
Taylor, Jeremy M. G., et al.. (2024). Robust data integration from multiple external sources for generalized linear models with binary outcomes. Biometrics. 80(1). 2 indexed citations
7.
Sun, Yuming, Stephen Salerno, Xinan Wang, et al.. (2023). Assessing the Prognostic Utility of Clinical and Radiomic Features for COVID-19 Patients Admitted to ICU: Challenges and Lessons Learned. SHILAP Revista de lepidopterología. 6(1). 1 indexed citations
8.
Yocum, Anastasia K., E Friedman, Holli Bertram, Peisong Han, & Melvin G. McInnis. (2023). Comparative mortality risks in two independent bipolar cohorts. Psychiatry Research. 330. 115601–115601. 3 indexed citations
9.
Han, Peisong, et al.. (2023). Improving estimation efficiency for two-phase, outcome-dependent sampling studies. Electronic Journal of Statistics. 17(1). 2 indexed citations
10.
Sun, Yuming, Stephen Salerno, Xinwei He, et al.. (2023). Use of machine learning to assess the prognostic utility of radiomic features for in-hospital COVID-19 mortality. Scientific Reports. 13(1). 7318–7318. 7 indexed citations
11.
Han, Peisong, et al.. (2023). Robust causal inference of drug‐drug interactions. Statistics in Medicine. 42(7). 970–992. 2 indexed citations
12.
Chen, Chixiang, Peisong Han, & Fan He. (2021). Improving main analysis by borrowing information from auxiliary data. Statistics in Medicine. 41(3). 567–579. 9 indexed citations
13.
Han, Peisong, et al.. (2021). Estimating the marginal hazard ratio by simultaneously using a set of propensity score models: A multiply robust approach. Statistics in Medicine. 40(5). 1224–1242. 4 indexed citations
14.
Parks, Courtney A., et al.. (2021). Reducing food insecurity and improving fruit and vegetable intake through a nutrition incentive program in Michigan, USA. SSM - Population Health. 15. 100898–100898. 17 indexed citations
15.
Salerno, Stephen, Yuming Sun, Xinwei He, et al.. (2021). Comprehensive evaluation of COVID-19 patient short- and long-term outcomes: Disparities in healthcare utilization and post-hospitalization outcomes. PLoS ONE. 16(10). e0258278–e0258278. 11 indexed citations
16.
Yocum, Anastasia K., et al.. (2021). Covid-19 pandemic and lockdown impacts: A description in a longitudinal study of bipolar disorder. Journal of Affective Disorders. 282. 1226–1233. 36 indexed citations
17.
Han, Peisong, et al.. (2019). Empirical likelihood inference for non-randomized pretest-posttest studies with missing data. Electronic Journal of Statistics. 13(1). 6 indexed citations
18.
Sun, Yilun, Lu Wang, & Peisong Han. (2019). Multiply robust estimation in nonparametric regression with missing data. Journal of nonparametric statistics. 32(1). 73–92. 3 indexed citations
19.
Han, Peisong. (2015). Combining Inverse Probability Weighting and Multiple Imputation to Improve Robustness of Estimation. Scandinavian Journal of Statistics. 43(1). 246–260. 36 indexed citations
20.
Han, Peisong, Peter X.‐K. Song, & Lu Wang. (2014). Achieving semiparametric efficiency bound in longitudinal data analysis with dropouts. Journal of Multivariate Analysis. 135. 59–70. 2 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.

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