Jun Yan
- Statistics and Probability top 0.2%
- Statistical Methods and Bayesian Inference 42
- Statistical Methods and Inference 38
- Statistical Distribution Estimation and Applications 18
- Advanced Causal Inference Techniques 10
- Finance top 1%
- Financial Risk and Volatility Modeling 25
- Medical Terminology top 2%
- Global and Planetary Change top 2%
- Hydrology and Drought Analysis 18
- Climate variability and models 12
- Developmental Biology top 5%
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- Bayesian Methods and Mixture Models 11
- Co-authors
- Ulrich HalekohSøren HøjsgaardIvan KojadinovicJason P. FineRobert H. AseltineXuebin ZhangSaikat MaitraMekonnen Gebremichael
- Journals
- Journal of the American Statistical Association (9 papers)Journal of Statistical Software (7 papers)Statistics and Computing (7 papers)
- Partner nations
- United StatesChinaCanada
In The Last Decade
Jun Yan
137 papers receiving 5.2k citations
Hit Papers
Peers
Comparison fields: 5 of 223
- Statistics and Probability 1.1k
- Finance 552
- Medical Terminology 11
- Global and Planetary Change 766
- Developmental Biology 76
Countries citing papers authored by Jun Yan
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
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
The 25 scholars most cited alongside Jun Yan, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 2 | |
| 2 | 2025 | 0 | |
| 3 | 2024 | 2 | |
| 4 | 2024 | 1 | |
| 5 | 2024 | 1 | |
| 6 | 2024 | 5 | |
| 7 | 2023 | 3 | |
| 8 | 2023 | 2 | |
| 9 | 2023 | 0 | |
| 10 | 2022 | 9 | |
| 11 | 2022 | 31 | |
| 12 | 2021 | 3 | |
| 13 | 2020 | 0 | |
| 14 | 2018 | 13 | |
| 15 | 2018 | 6 | |
| 16 | 2017 | 26 | |
| 17 | 2017 | 9 | |
| 18 | Enjoy the Joy of Copulas: With a Package copula | 2015 | 9 |
| 19 | Practical Notes On Multivariate Modeling Based on Elliptical Copulas | 2013 | 4 |
| 20 | A goodness-of-fit test for multivariate multiparameter copulas based on multiplier central limit theorems | 2011 | 2 |
About Jun Yan
Jun Yan is a scholar working on Statistics and Probability, Finance and Global and Planetary Change, having authored 146 papers that have together received 5.4k indexed citations. Recurring topics across this work include Statistical Methods and Bayesian Inference (42 papers), Statistical Methods and Inference (38 papers), Financial Risk and Volatility Modeling (25 papers), Hydrology and Drought Analysis (18 papers), Statistical Distribution Estimation and Applications (18 papers), Climate variability and models (12 papers), Bayesian Methods and Mixture Models (11 papers) and Advanced Causal Inference Techniques (10 papers). The work is most often cited by research in Statistics and Probability (1.1k citations), Finance (552 citations) and Medical Terminology (11 citations). Jun Yan has collaborated with scholars based in United States, China and Canada. Frequent co-authors include Ulrich Halekoh, Søren Højsgaard, Ivan Kojadinovic, Jason P. Fine, Robert H. Aseltine, Xuebin Zhang, Saikat Maitra, Mekonnen Gebremichael, Sy Han Chiou and Daniel Chen. Their work appears in journals such as Journal of the American Statistical Association, Journal of Statistical Software, Statistics and Computing, Statistics in Medicine and The Annals of Applied Statistics.
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.