Jun Yan

7.9k total citations · 1 hit paper
146 papers, 5.4k citations indexed

About

Jun Yan is a scholar working on Statistics and Probability, Finance and Global and Planetary Change. According to data from OpenAlex, Jun Yan has authored 146 papers receiving a total of 5.4k indexed citations (citations by other indexed papers that have themselves been cited), including 67 papers in Statistics and Probability, 29 papers in Finance and 24 papers in Global and Planetary Change. Recurrent topics in Jun Yan's work include Statistical Methods and Bayesian Inference (42 papers), Statistical Methods and Inference (38 papers) and Financial Risk and Volatility Modeling (25 papers). Jun Yan is often cited by papers focused on Statistical Methods and Bayesian Inference (42 papers), Statistical Methods and Inference (38 papers) and Financial Risk and Volatility Modeling (25 papers). Jun Yan collaborates with scholars based in United States, China and Canada. Jun Yan's co-authors include Søren Højsgaard, Ulrich Halekoh, Ivan Kojadinovic, Jason P. Fine, Robert H. Aseltine, Xuebin Zhang, Saikat Maitra, Mekonnen Gebremichael, Sy Han Chiou and Daniel Chen and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of the American Statistical Association and Ecology.

In The Last Decade

Jun Yan

137 papers receiving 5.2k citations

Hit Papers

TheRPackagegeepackfor Generalized Estimating Equations 2006 2026 2012 2019 2006 400 800 1.2k
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. TheRPackagegeepackfor Generalized Estimating Equations
    2006 1.5k citations Jun Yan et al. Journal of Statistical Software 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 Yan United States 32 1.1k 766 552 495 422 146 5.4k
Robert A. Rigby United Kingdom 22 1.1k 1.0× 941 1.2× 240 0.4× 456 0.9× 458 1.1× 51 5.2k
D. Stasinopoulos United Kingdom 15 828 0.8× 830 1.1× 205 0.4× 372 0.8× 439 1.0× 28 4.4k
Thomas M. Loughin United States 27 1.2k 1.1× 590 0.8× 262 0.5× 479 1.0× 193 0.5× 106 7.0k
Nicolás Chopin France 32 1.2k 1.1× 518 0.7× 225 0.4× 671 1.4× 422 1.0× 104 6.9k
Christian Kleiber Germany 17 652 0.6× 914 1.2× 407 0.7× 977 2.0× 168 0.4× 37 5.3k
Thomas Kneib Germany 40 1.8k 1.6× 744 1.0× 210 0.4× 928 1.9× 205 0.5× 211 7.3k
Robert L. Strawderman United States 31 1.2k 1.1× 1.0k 1.3× 325 0.6× 714 1.4× 298 0.7× 98 9.1k
Adrian Bowman United Kingdom 35 1.6k 1.5× 700 0.9× 305 0.6× 591 1.2× 205 0.5× 135 8.2k
Louis‐Paul Rivest Canada 26 1.4k 1.3× 778 1.0× 1.4k 2.5× 646 1.3× 102 0.2× 130 4.3k
Leonhard Held Switzerland 45 1.8k 1.7× 474 0.6× 192 0.3× 1.1k 2.3× 555 1.3× 212 8.8k

Countries citing papers authored by Jun Yan

Since Specialization
Citations

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

Fields of papers citing papers by Jun Yan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jun Yan

This figure shows the co-authorship network connecting the top 25 collaborators of Jun Yan. A scholar is included among the top collaborators of Jun Yan 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 Yan. Jun Yan 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
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Chandy, Mathew J., Elizabeth D. Schifano, & Jun Yan. (2024). On Sample Size Needed for Block Bootstrap Confidence Intervals to Have Desired Coverage Rates. 20(4). 3–16. 1 indexed citations
4.
Yan, Jun, et al.. (2024). On the maximum likelihood estimation of a discrete, finite support distribution under left-truncation and competing risks. Statistics & Probability Letters. 207. 109973–109973. 2 indexed citations
5.
Schifano, Elizabeth D., et al.. (2024). On Misuses of the Kolmogorov–Smirnov Test for One-Sample Goodness-of-Fit. The American Statistician. 78(4). 481–487. 5 indexed citations
6.
Wang, Tiandong, et al.. (2023). Generating General Preferential Attachment Networks with R Package wdnet. Journal of Data Science. 538–556.
7.
Yan, Jun, et al.. (2023). Estimating a discrete distribution subject to random left-truncation with an application to structured finance. Econometrics and Statistics. 37. 174–198. 3 indexed citations
8.
Wang, Tianying, et al.. (2023). Optimal Fingerprinting with Estimating Equations. Journal of Climate. 36(20). 7109–7122. 3 indexed citations
9.
Guo, Xudong, et al.. (2022). PaCAR: COVID-19 Pandemic Control Decision Making via Large-Scale Agent-Based Modeling and Deep Reinforcement Learning. Medical Decision Making. 42(8). 1064–1077. 9 indexed citations
10.
Yan, Jun, et al.. (2022). Application of big data and artificial intelligence in epidemic surveillance and containment. PubMed. 3(1). 36–43. 31 indexed citations
11.
Meyer, Thomas H., et al.. (2021). Moving‐resting process with measurement error in animal movement modeling. Methods in Ecology and Evolution. 12(11). 2221–2233. 3 indexed citations
12.
Yan, Jun, et al.. (2020). Dragon’s Abacus: Developmentalist Strategy and EconomicSocial Consequences of Chinese Companies in Africa. İstanbul Üniversitesi Sosyoloji Dergisi / İstanbul University Journal of Sociology. 40(2).
13.
Caplan, Daniel J., et al.. (2018). Dental Restoration Longevity among Geriatric and Special Needs Patients. JDR Clinical & Translational Research. 4(1). 41–48. 13 indexed citations
14.
Chiou, Sy Han, Chiung‐Yu Huang, Gongjun Xu, & Jun Yan. (2018). Semiparametric Regression Analysis of Panel Count Data: A Practical Review. International Statistical Review. 87(1). 24–43. 6 indexed citations
15.
Wang, Chun, Ming‐Hui Chen, Jing Wu, et al.. (2017). Online updating method with new variables for big data streams. Canadian Journal of Statistics. 46(1). 123–146. 26 indexed citations
16.
Chiou, Sy Han, Gongjun Xu, Jun Yan, & Chiung‐Yu Huang. (2017). Semiparametric Estimation of the Accelerated Mean Model with Panel Count Data Under Informative Examination Times. Biometrics. 74(3). 944–953. 9 indexed citations
17.
Wang, Xiaojing & Jun Yan. (2013). Practical Notes On Multivariate Modeling Based on Elliptical Copulas. French digital mathematics library (Numdam). 154(1). 102–115. 4 indexed citations
18.
Yan, Jun, et al.. (2011). Trend analysis of extreme precipitation in the Northwestern Highlands of Ethiopia with a case study of Debre Markos. Hydrology and earth system sciences. 15(6). 1937–1944. 50 indexed citations
19.
Kojadinovic, Ivan & Jun Yan. (2011). A goodness-of-fit test for multivariate multiparameter copulas based on multiplier central limit theorems. SPIRE - Sciences Po Institutional REpository. 2 indexed citations
20.
Kojadinovic, Ivan & Jun Yan. (2010). Modeling Multivariate Distributions with Continuous Margins Using the copula R Package. Journal of Statistical Software. 34(9). 285 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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