Jun Shao

1.0k total citations · 1 hit paper
13 papers, 646 citations indexed

About

Jun Shao is a scholar working on Statistics and Probability, Economics and Econometrics and Electrical and Electronic Engineering. According to data from OpenAlex, Jun Shao has authored 13 papers receiving a total of 646 indexed citations (citations by other indexed papers that have themselves been cited), including 3 papers in Statistics and Probability, 2 papers in Economics and Econometrics and 2 papers in Electrical and Electronic Engineering. Recurrent topics in Jun Shao's work include Statistical Methods and Bayesian Inference (2 papers), Power Systems and Technologies (2 papers) and Advanced Statistical Process Monitoring (2 papers). Jun Shao is often cited by papers focused on Statistical Methods and Bayesian Inference (2 papers), Power Systems and Technologies (2 papers) and Advanced Statistical Process Monitoring (2 papers). Jun Shao collaborates with scholars based in China, United States and Canada. Jun Shao's co-authors include Shein‐Chung Chow, Hansheng Wang, Yuliya Lokhnygina, Jae Kwang Kim, Fei Zheng, Kaizhen Zhang, Lianghu Wang, Dianfu Ma, Xuan Li and Yongwang Zhao and has published in prestigious journals such as Journal of the American Statistical Association, Journal of the Royal Statistical Society Series C (Applied Statistics) and Quality Engineering.

In The Last Decade

Jun Shao

12 papers receiving 622 citations

Hit Papers

Sample Size Calculations in Clinical Research: Third Edition 2017 2026 2020 2023 2017 50 100 150 200 250
What are hit papers?

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.

  1. Sample Size Calculations in Clinical Research: Third Edition
    2017 298 citations Shein‐Chung Chow, Jun Shao et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jun Shao China 7 192 65 43 36 36 13 646
Dimitris Fouskakis Greece 11 121 0.6× 103 1.6× 29 0.7× 25 0.7× 34 0.9× 34 673
Xiaoke Zhang United States 12 245 1.3× 98 1.5× 26 0.6× 33 0.9× 29 0.8× 44 592
Bruno Scherrer France 17 118 0.6× 185 2.8× 25 0.6× 70 1.9× 63 1.8× 53 1.5k
Mary C. Meyer United States 16 315 1.6× 112 1.7× 67 1.6× 38 1.1× 46 1.3× 44 681
Yazhen Wang United States 12 130 0.7× 60 0.9× 54 1.3× 91 2.5× 23 0.6× 24 744
Brenda MacGibbon Canada 12 218 1.1× 70 1.1× 27 0.6× 72 2.0× 51 1.4× 32 795
Luo Xiao United States 14 248 1.3× 127 2.0× 23 0.5× 30 0.8× 21 0.6× 56 726
Jennifer Schumi United States 9 84 0.4× 68 1.0× 36 0.8× 89 2.5× 42 1.2× 16 663
Juha Karvanen Finland 25 167 0.9× 125 1.9× 27 0.6× 75 2.1× 47 1.3× 81 1.5k

Countries citing papers authored by Jun Shao

Since Specialization
Citations

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

Fields of papers citing papers by Jun Shao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jun Shao

This figure shows the co-authorship network connecting the top 25 collaborators of Jun Shao. A scholar is included among the top collaborators of Jun Shao 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 Jun Shao. Jun Shao is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

13 of 13 papers shown
1.
Shao, Jun & Lianghu Wang. (2022). Can New-Type Urbanization Improve the Green Total Factor Energy Efficiency?   Evidence from China. SSRN Electronic Journal. 2 indexed citations
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5.
Chow, Shein‐Chung, Jun Shao, Hansheng Wang, & Yuliya Lokhnygina. (2017). Sample Size Calculations in Clinical Research: Third Edition. 298 indexed citations breakdown →
6.
Kim, Jae Kwang & Jun Shao. (2013). Statistical Methods for Handling Incomplete Data. 80 indexed citations
7.
Shao, Jun. (2008). Handling survey nonresponse in cluster sampling. Quality Engineering. 53(4). 381–384. 8 indexed citations
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9.
Chow, Shein‐Chung, Jun Shao, Hansheng Wang, & Yuliya Lokhnygina. (2007). Bayesian Sample Size Calculation. 341–368. 1 indexed citations
10.
Shao, Jun. (1993). Linear Model Selection by Cross-Validation. Journal of the American Statistical Association. 88(422). 486–486. 215 indexed citations
11.
Shao, Jun & Shein‐Chung Chow. (1991). Constructing Release Targets for Drug Products: A Bayesian Decision Theory Approach. Journal of the Royal Statistical Society Series C (Applied Statistics). 40(3). 381–381. 8 indexed citations
12.
Shao, Jun. (1989). Monte Carlo Approximations in Bayesian Decision Theory. Journal of the American Statistical Association. 84(407). 727–727. 5 indexed citations
13.
Shao, Jun. (1989). The Efficiency and Consistency of Approximations to the Jackknife Variance Estimators. Journal of the American Statistical Association. 84(405). 114–114. 11 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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