Jun Yan
Department of Statistics
University of Connecticut
215 Glenbrook Rd. U-4120
Storrs, CT 06269
Phone: (860) 486-3416
Fax: (860) 486-4113
Email: jun.yan@uconn.edu
Web: https://statcomp.org/
Degrees
Ph.D., 2003, Statistics, University of Wisconsin, Madison, WI
M.A., 1998, Economics, University of Miami, Miami, FL
M.Econ., 1996, Statistics, Renmin University of China, Beijing, China
B.Econ., 1993, Statistics, Renmin University of China, Beijing, China
Research Interests
Social Network, Spatial Extremes, Dynamic Survival Models, Multivariate Dependence, Clustered Data Analysis
Statistical Computing, Biostatistics, Environmental Applications, Public Health Applications, Sports Analytics
Positions
Professor, Department of Statistics, University of Connecticut, 2015–Present.
Affiliated Faculty, The Connecticut Institute for the Brain and Cognitive Sciences, 2023–Present.
Research Fellow, Center for Population Health, University of Connecticut Health Center, 2007–Present.
Associate Professor, Department of Statistics, University of Connecticut, 2010–2015.
Affiliated Faculty, Center for Environmental Sciences and Engineering, University of Connecticut, 2007–Present.
Assistant Professor, Department of Statistics, University of Connecticut, 2007–2010.
Assistant Professor, Department of Statistics and Actuarial Science, The University of Iowa, 2003–2007.
Statistical Consultant, CALS Statistical Consulting Lab, University of Wisconsin–Madison, 2001–2003.
Honors and Awards
Elected Member, 2023, Connecticut Academy of Science and Engineering
Fellow, 2022, Institute of Mathematical Statistics
Fellow, 2017, American Statistical Association
Elected Member, 2014, International Statistical Institute
Outstanding Service Award, 2014, International Chinese Statistical Association
Professional Memberships
American Association for the Advancement of Science (AAAS)
American Geophysical Union (AGU)
American Statistical Association (ASA)
International Chinese Statistical Association (ICSA)
Institute of Mathematical Statistics (IMS)
Services and Outreaches
Editorial
1. 2020–present: Editor-in-Chief Journal of Data Science.
2. 2020–2021: Associate editor, Brazilian Journal of Probability and Statistics.
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3. 2020: Co-Guest Editor, Special Issue on “Data Science in Action in Response to the Outbreak of COVID-19”, Journal
of Data Science.
4. 2014–2020: Associate editor, Ecological and Environmental Statistics.
5. 2014: Associate editor, STEM Forums in Statistics.
Professional Society Services
1. 2025–2027: Chair-Elect/Chair/Past-Chair of the ASA Section on Statistical Learning and Data Science.
2. 2024–2025: ASA Representative, ACM ASA MAA SIAM Joint Taskforce on Undergraduate Data Science Compe-
tencies.
3. 2022–2024: Member, ASA Committee on Data Science and AI
4. 2021–2023: Chair-Elect/Chair/Past-Chair of the ASA Section on Statistical Computing.
5. 2020: Co-Chair, ASA–Journal of Data Science webinar series on “Data Science in Action in Response to the Out-
break of COVID-19”, April 17 — July 24, 2020.
6. 2018–2021: Member, IMS Committee to Select Editors.
7. 2017–2020: Awards Chair, ASA Section on Statistical Computing and Section on Statistical Graphics.
8. 2016–2020, Editor, LIDA Newsletter, The official newsletter of the Lifetime Data Analysis Section, American Statisti-
cal Association.
9. 2015–2016: Chair, IT Committee, International Chinese Statistical Association.
10. 2012–2014: Editor-in-Chief, International Chinese Statistical Association Bulletin.
Conference Organization
1. 2024: Co-Chair, International Forum on Data Science 2024.
2. 2024: Lead Organizer, Banff International Research Station Workshop 24w5284: Statistical Aspects of Trustworthy
Machine Learning (with Stephanie Hicks, Keegan Korthauer, Xiaotong Shen, and Helen Zhang)
3. 2019–present: Chair, Organizing Committee, Annual UConn Sports Analytics Symposium.
4. 2022: Chair, Organizing Committee, Excellence in Statistical Science: Celebrating the 60th Anniversary of UConn
Statistics.
5. 2022: Chair, Steering Committee, ASA Section on Statistical Computing Mini-Symposium: Statistical Computing in
Action.
6. 2022: Program Committee, 2023 ICSA Applied Statistics Symposium.
7. 2020: Chair, Organizing Committee, The 3rd International Forum on Statistical Science and Big Data, June 13–15,
2020, School of Statistics, Shanxi University of Finance and Economics.
8. 2019: Chair, Organizing Committee, 33rd New England Statistics Symposium: Statistical Data Science in Action.
9. 2018: Organizing Committee, The 8th International Statistics Forum, Renmin University of China.
10. 2018: Co-Chair, Short-course Committee, 2018 ICSA Applied Statistics Symposium.
11. 2017: Co-Chair, Local Organizing Committee, Conference on Lifetime Data Science: Data Science, Precision
Medicine and Risk Analysis with Lifetime Data at University of Connecticut, May 25–27, 2017, Sponsored by the
Lifetime Data Analysis Interest Group, American Statistical Association.
12. 2015: Chair, Poster Committee, 2015 ICSA Applied Statistics Symposium
Grant Reviews
1. NSF CAIG panel review, 2024.
2. NSF DMS CDSE-MSS panel review, 2016.
3. NSF DMS Statistics panel review, 2013.
Journal Reviews
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1. Annals of Applied Statistics; Annals of Statistics; Applied Stochastic Models in Business and Industry; Bernoulli;
Biometrics; Biometrika; Bioscience; Canadian Journal of Statistics; Computational Statistics; Computational Statis-
tics and Data Analysis; Econometrics and Statistics; Environmental and Ecological Statistics; IEEE Transactions
on Neural Networks; International Journal of Forecasting; Journal of the American Statistical Association ;Journal
of Data Science; Journal of Multivariate Statistics; Journal of Royal Statistical Society Series B; Journal of Royal
Statistical Society Series C; Journal of Statistical Software; R Journal; Statistics and Probability Letters; Statistica
Sinica; Statistics in Medicine; Stochastic Environmental Research and Risk Assessment; among others.
2. Statistical Reviewer: Journal of Applied Physiology; Advances in Physiology Education.
Departmental Service
1. 2023–2024: Promotion, Tenure, and Reappointment Committee (chair); X + 3 + 1 Admission (chair); Search Com-
mittee for Assistant Professors; Computer Committee; Graduate Examination Committee.
2. 2022–2023: Promotion, Tenure, and Reappointment Committee (chair); 10-Year Strategic Plan Committee (chair);
Organizing Committee of the 60th Anniversary Celebration (chair); X + 3 + 1 Admission (chair); Search Committee for
Assistant Professors; Computer Committee; Graduate Examination Committee; SET+ Faculty Teaching Evaluations.
3. 2021–2022: Promotion, Tenure, and Reappointment Committee (chair); 10-Year Strategic Plan Committee (chair);
Organizing Committee of the 60th Anniversary Celebration (chair); X + 3 + 1 Admission (chair); Search Committee
for Assistant Professor in Data Science; Computer Committee; Graduate Examination Committee.
4. 2020–2021: Data Science Program (chair), Computer Committee (chair), Graduate Examination, X + 3 + 1 Admis-
sion (Chair), Promotion, Tenure, and Reappointment Committee.
5. 2019–2020: Data Science Program (chair), Computer Committee (chair), Graduate Examination, X + 3 + 1 Admis-
sion, Promotion, Tenure, and Reappointment Committee.
6. 2018–2019: Search Committee (chair), Data Science Program (chair), Computer Committee (chair), Graduate Ex-
amination, Distinguished Statistician Series, 3 + 1 Admission, Promotion, Tenure, and Reappointment Committee.
7. 2017–2018: Data Science Program (chair), Computer Committee (chair), Graduate Examination, Distinguished
Statistician Series, 3 + 1 Admission, Graduate Admission Committee, Promotion, Tenure, and Reappointment Com-
mittee.
8. 2016–2017: Computer Committee (chair), Program Review Committee, Graduate Students and Distinguished Alumni
Awards Committee, X + 3 + 1 Admission, New England Statistics Symposium (chair), New England Statistics Society,
Faculty Search Committee, Graduate Admission Committee, Promotion, Tenure, and Reappointment Committee.
9. 2015–2016: Computer Committee (chair), Admission Committee, Department Head Search Committee, Promotion,
Tenure, and Reappointment Committee.
10. 2014–2015: Computer Committee (chair), Admission Committee, New England Statistics Symposium Committee.
11. 2013–2014: Computer Committee (chair), Admission Committee.
12. 2012–2013: Computer Committee (chair), Associate/Full professor search committee, Department’s 50th Year Cel-
ebration committee, New England Statistics Symposium Committee, Admission Committee.
13. 2011–2012: Computer Committee (chair), Admissions Committee, Biostatistics Search Committee, Department’s
50th Year Celebration committee, Biostatistics Program Development Committee.
14. 2010–2011: Colloquium Committee (chair), Library/Tech Reports Committee, Search Committee, Biostatistics Pro-
gram Development Committee, New England Statistics Symposium Committee.
15. 2008–2010: Library/Tech Reports Committee, Biostatistics Program Development Committee, Social Committee.
16. 2006–2007: Colloquium Committee (chair), M.S. Exam – Minor Committee (chair), Computer Committee.
17. 2005–2006: Colloquium Committee, M.S. Exam – Minor Committee, Search Committee, and Social Committee.
18. 2004–2005: M.S. Exam – Minor Committee, Search Committee, and Social Committee (chair).
19. 2003–2004: Computer Committee, M.S. Exam – Minor Committee, and Social Committee.
Outreaches
1. 2023–present: New York City Open Data Ambassador, working with libraries and community organizations to help
bridging data literacy gaps and promoting neighborhood and issue-based dialogue.
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2. 2022–present: Instructor of Introduction to Data Science, one-week intensive Pre-College Summer course for high
schoolers.
3. 2019–2019: Instructor of Coding for Kids (6–8 years old), Connecticut Chinese Language Academy.
Media Coverage
1. UConn Today (2024): UConn Sports Analytics Symposium Showcases the Numbers Behind the Games.
2. UConn Today (2023): UConn Students to Showcase Work at NYC Open Data Week.
3. ASA Member News (2023): Data is My Job.
4. AMStat News (2023): UConn Department of Statistics Celebrates 60th Anniversary.
5. AMStat News (2023): UConn Sports Analytics Symposium Boasts New Features.
6. UConn Today (2022): From Wyoming Mountains to Connecticut Forests, Tracking Feline Apex Predators.
7. Methods Blog (Methods in Ecology and Evolution) (2022): Revealing the hidden lives of cryptic mountain lions using
GPS data and a Moving-Resting Motion model.
8. AMStat News (2022): UConn Sports Analytics Symposium a Home Run.
9. UConn Today (2020): UConn Holds Second Annual Sports Analytics Conference.
10. UConn Today (2020): CLAS Faculty and Students Shifting Work to COVID-19.
11. UConn Today (2019): UConn Hosts New Sports Analytics Symposium.
Teaching Experience
University of Connecticut
1. Data Science: One-week intensive Pre-College Summer course for high schoolers; Summer 2021; Summer 2022;
Summer 2023.
2. Special topics: Advanced Data Manipulation and Analysis with Python (4185), 1-credit undergraduate level: Fall
2022; Spring 2023; Fall 2023.
3. Undergraduate Seminar/Investigation of Special Topics: Statistical Writing (3494W/5095): 1-credit graduate/undergraduate
statistical literacy requirement; Fall 2022.
4. Introduction to Data Science (5255/3255): 3-credit graduate/undergraduate level; Fall 2021; Spring 2022; Spring
2023; Spring 2024.
5. Biostatistics (5625/4625), 3-credit graduate/undergraduate level; Spring 2021.
6. Data Science in Action (6494), 3-credit seminar; Spring 2018 (co-taught with Kun Chen and Elizabeth Schifano);
Spring 2019.
7. Statistical Computing (5361), 3-credit graduate level; Spring 2018; Fall 2018; Fall 2020.
8. Applied Statistics II (5605), 3-credit graduate level (required for qualifying exam); Spring 2017.
9. Multivariate Modeling with Copulas (6494), 3-credit graduate level; Fall 2016.
10. Advanced Statistical Computing (6494), 3-credit graduate level; Spring 2014/2015/2016.
11. Applied Longitudinal Data Analysis (6494), 3-credit graduate level; Fall 2014.
12. Mathematical Statistics I/II (5585/5685), 3-credit graduate level sequence (required for qualifying exam); Fall 2011
— Spring 2013, Fall 2007 — Spring 2010, Fall 2015, Fall 2017.
13. Data Analysis Using R (6494), 3-credit graduate level; Spring 2011.
14. Environmental Statistics (6494), 3-credit graduate level; Fall 2010.
15. Statistical Methods (220), 3-credit undergraduate level; Spring 2008.
University of Iowa
1. Probability and Stochastic Processes I (22s:195), Fall 2006.
2. Mathematical Statistics II (22s:154), Spring 2007, Spring 2006, Spring 2004.
3. Mathematical Statistics I (22s:153), Fall 2005, Fall 2004, Fall 2003.
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4. Applied Time Series Analysis (22s:156), Spring 2007, Spring 2005.
Students
Ph.D. Thesis Advisees
1. Qingkai Dong, Ph.D. expected 2028 (joint with HaiYing Wang): Subsampling and adaptive design.
2. Shiying Xiao, Ph.D. expected 2027: Network analytics.
3. Zefang Min, Ph.D. expected 2026: Time series and causal inference.
4. Xiaomin Lu, Ph.D. expected 2025: Survival analysis and causal inference.
5. Sydney Louit, Ph.D. expected 2025: Network analytics.
6. Zhenyu Xu, Ph.D. expected 2025: Cure rate and competing risk.
7. Jun Bruce Jin, Ph.D. 2024 (joint with Kun Chen): On large-scale transfer learning with heterogeneous data. Place-
ment: Faculty Biostatistician, Henry Ford Health.
8. Surya Eada, Ph.D. 2024 (joint with Vladimir Pozdnyakov): L
´
evy process governed by telegraph signal process:
Statistical Inferences and applications. Placement: Assistant Professor of Teaching, Oregon State University.
9. Yingfa Xie, Ph.D. 2024: Recurrent events modeling based on a reflected Brownian motion with application to hypo-
glycemia. Placement: Postdoc researcher, Yale University.
10. Lucas Godoy, Ph.D. 2024: Hausdorff-Gaussian process with spatial and spatiotemporal applications. Placement:
Postdoc researcher, University of California at Santa Cruz.
11. Yelie Yuan, Ph.D. 2023: On assortativity of weighted directed networks. Placement: Consumer and Community
Banking Risk Program Associate, JP Morgan.
12. Zehan Yang, Ph.D. 2023 (joint with HaiYing Wang): Optimal subsampling methods for massive survival data using
accelerated failure time models. Placement: Mathematical Statistician, US Food and Drug Administration.
13. Jackson Lautier, Ph.D. 2023 (joint with Vladimir Pozdnyakov): Essays on discrete-time survival analysis with appli-
cations to securitization and consumer finance. Placement: Assistant Professor, Bentley University.
14. Sai Ma, Ph.D. 2022: Optimal fingerprinting with estimating equations. Placement: Statistician, Vertex Pharmaceuti-
cals.
15. Abby Lau, Ph.D. 2022: Extreme value modeling with errors-in-variables in detection and attribution of changes in
climate extremes. Placement: Postdoc researcher, University of Pennsylvania.
16. Yan Li, Ph.D. 2021 (joint with Kun Chen): Amalgamation-based statistical learning for compositional data. Place-
ment: Postdoc researcher, University of Michigan.
17. Jieying Jiao, Ph.D. 2020: On Bayesian methods for spatial point processes. Placement: Senior Consultant, Travelers
Insurance.
18. Chaoran Hu, Ph.D. 2020 (joint with Vladimir Pozdnyakov): On Brownian motion governed by telegraph process.
Placement: Research Scientist, Ely Lilly and Company.
19. Wenjie Wang, Ph.D. 2019 (joint with Kun Chen): Integrated survival analysis with application to suicide risk. Place-
ment: Research Scientist, Eli Lilly and Company.
20. Yishu Xue, Ph.D. 2019 (joint with Elizabeth Schifano): Diagnostic methods for big survival data. Placement: Senior
Consultant, Travelers Insurance.
21. Greg Vaughan, Ph.D. 2017 (joint with Kun Chen): Stagewise generalized estimating equations. Placement: Assistant
Professor, Bentley University.
22. Yujing Jiang, Ph.D. 2017: Marginal score equations for spatial extremes with latent signals and applications to
fingerprinting changes in climate extremes. Placement: Postdoc researcher, Colorado State University.
23. Brian Bader, Ph.D. 2016: Automated, efficient, and practical extreme value analysis with environmental applications.
Placement: Statistician, KPMG.
24. Chun Wang, Ph.D. 2016 (joint with Elizabeth Schifano): Online updating methods for big data streams. Placement:
Senior Analyst, Liberty Mutual Insurance.
25. Zhuo Wang, Ph.D. 2015: Estimating equations for spatial extremes with application to detection and attribution
analysis of changes in climate extremes. Placement: Assistant Professor, Shenzhen University, China.
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26. Hongwei Shang: Ph.D. 2013: A Two-step estimation procedure and a goodness-of-fit test for spatial extremes
models. Placement: Statistician, HP Analytic Lab.
27. Sy Han (Steven) Chiou, Ph.D. 2013 (joint with Sangwook Kang): Statistical methods and computing for semipara-
metric accelerated failure time model with induced smoothing. Placement: Assistant Professor, Department of
Mathematics and Statistics, University of Minnesota Duluth.
28. Xiaojing Wang: Ph.D. 2011: Statistical inferences for interval censored data. Placement: Quantitative Analyst,
Google New York.
29. Marcos Prates, Ph.D. 2011 (joint with Dipak Dey): Link specification and spatial dependence for generalized lin-
ear mixed models. Placement: Assistant Professor, Departamento de Estat
´
ıstica, Universidade Federal de Minas
Gerais, Brazil.
Undergraduate Thesis Advisees
1. Mathew Chandy (2024): Nonparametric block bootstrap Kolmogorov–Smirnov goodness-of-fit test. Placement:
Ph.D. Student in Statistics, UCLA.
2. Kathleen Houlihan (2024): Selecting team members for the female Artistic Gymnastics Team USA for the Paris
Olympics. Placement: Dental School student, Boston University.
3. Shannon Yeung (2023): Varying effects of short term interest rates. Placement: GMI Analyst, BNY Mellon.
4. Owen Fiore (2023): Was Devon Allen unjustly disqualified at the 2022 World Track and Field Championships?
Placement: MS Student in Data Science, University of Connecticut.
5. Pranav Tavildar (2023): Sentiment analysis of twitter in relation to fossil fuel stock Prices. Placement: MS Student
in Data Analytics, Georgia Institute of Technology.
6. Samuel Hughes (2022): Statistical evaluation of field hockey penalty corners. Placement: MS Student in Data
Science, Northeastern University.
7. Anthony Zeimbekakis (2022): On misuses of the Kolmogorov-–Smirnov test for one-sample goodness-of-fit. Place-
ment: Analyst Development Program, Electric Insurance Company.
8. Brian Krikorian (2022): Points above replacement: A new NBA metric to evaluate player performance. Placement:
Success Metrics Intern, UMass Chan Medical School.
9. Justin Franklin (2021): Comparison of fraud detection methods: A case study with insurance. Placement: Software
Engineer, Travelers.
10. Andrew Tammaro (2021): NFL front office analytics with R. Placement: Corporate Strategy and Data Analytics
Intern, 1BusinessWorld.
11. Michael Price (2021): The effects of the NBA COVID bubble on the NBA playoffs: A case study for home-court
advantage. Placement: MS program in Applied Statistics, University of Delaware.
12. Dylan Barrett (2020): When is the best time to steal bases? Placement: Customer Operations Agent, FanDuel.
13. Taaj Cheema (2020): An analysis of Oliver’s four factors in the golden age of NBA offense. Placement: Data
Scientist, IBM.
14. Jack Schooley (2020): Predicting the outcomes of soccer games. Placement: MS Student in Data Sience, MIT.
15. Thomas Kennon (2018): Finding an ultimate limit for an NBA player’s shooting percentage. Placement: Data Engi-
neer, The Hartford.
16. Junghi Kim (2010, Joint with Evarist Gin
´
e and Nalini Ravishanker): A close look at marginal Cox model and con-
ditional Cox model with application of recurrent gap times. Placement: Ph.D. Student in Biostatistics, University of
Minnesota.
Teaching Accomplishments
Student Awards
1. Jackson Lautier (2024): Honorable Mention, Arnold Zellner Thesis Award in Econometrics and Statistics, American
Statistical Association.
2. Sydney Louit (2024): Honorable Mention, Student Paper Award, 2024 Applied Statistics Symposium, International
Statistical Association.
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3. Lucas Godoy (2024): Honorable Mention, Student Paper Award, Section on Statistics and the Environment, Ameri-
can Statistical Association.
4. Yingfa Xie (2024): Student Paper Award, Section on Lifetime Data Science, American Statistical Association.
5. Zehan Yang (2023): Honorable Mention, Student Paper Award, 2023 Applied Statistics Symposium, International
Chinese Statistical Association.
6. Yelie Yuan (2023): Honorable Mention, John M. Chambers Statistical Software Award, American Statistical Associ-
ation.
7. Jackson Lautier (2022): Student Paper Award, Special Conference Celebrating the 60th Anniversary of UConn
Department of Statistics.
8. Zehan Yang (2022): Student Paper Award, Section on Lifetime Data Science, American Statistical Association.
9. Jackson Lautier (2022): Student Paper Award, Risk Analysis Section, American Statistical Association.
10. Jun Jin (2022): Honorable Mention, Student Paper Award, Risk Analysis Section, American Statistical Association.
11. Chaoran Hu (2020): Student Paper Award, Statistical Computing and Statistical Graphics Sections, American Sta-
tistical Association.
12. Yan Li (2020): ENAR Distinguished Student Paper Award, International Biometric Society.
13. Yan Li (2019): IBM Student Paper Award, 33rd New England Statistics Symposium.
14. Yishu Xue (2019): ENAR Distinguished Student Paper Award, International Biometric Society.
15. Wenjie Wang (2017): IBM Student Paper Award, 31st New England Statistics Symposium.
16. Gregory Vaughan (2017): Student Paper Award, Mental Health Section, American Statistical Association.
17. Steven Chiou (2012): Student Paper Award, Applied Statistics Symposium, International Chinese Statistical Associ-
ation.
18. Yung-wei Chen (2012): Student Paper Award, Social Statistics, Government Statistics, and Survey Research Meth-
ods Sections, American Statistical Association.
19. Xiaojing Wang (2010): IBM Student Paper Award, 24th New England Statistics Symposium.
Teaching Highlights
1. Undergraduate advisees have published journal articles since 2022.
2. Advised the Undergraduate Data Science Club as faculty advisor since 2019.
3. Co-taught “Data Science” to pre-college students in the UConn Pre-College Summer Program since 2020.
4. Developed new courses for the data science program: Statistical Data Science in Action; Introduction to Data Sci-
ence; Spatiotemporal Statistics.
5. Advised undergraduates in data science in UConn Individualized Major Program.
6. Mentored statistics students to develop software packages in R.
7. Taught students in ecology and evolutionary biology how to do data analysis with R.
8. Taught the first-year graduate required sequence of mathematical statistics effectively.
Grants
External
1. Connecticut Children’s Medical Center, 04/01/2024—03/31/2025: Predictive and Analytical Tools for Decision Mak-
ing at Connecticut Children’s Medical Center. $23,228.79. PI: Jun Yan.
2. Servier, 03/11/2024—12/31/2024: AI-Based Adaptive Clinical Trail. $21,666.67. PI: Jun Yan.
3. NSF DMS2219336, 09/01/2022 — 08/31/2025: Conference: UConn Sports Analytics Symposium: Engaging Stu-
dents into Data Science. $49,986. PI: Jun Yan; Co-PIs: Laura Burton, Kun Chen, Robert Huggins, Elizabeth
Schifano.
4. NSF DMS2210735: 08/01/2022 — 07/31/2025: Models and Inferences for Heterogeneous Interaction Patterns in
Social Networks. $360,000. PI: Jun Yan; Co-PI: Xianyang Zhang.
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5. NSF CC19325716, 08/01/2019 — 07/31/2021: CC* Compute: Shared Computing Infrastructure for Large-scale
Science Problems. $400,000. PI: Richard T. Jones, Co-PIs: Vernon Cormier, Kyungseon Joo, Cara D. Battersby,
and Jun Yan.
6. NSF DMS1521730, 2015/09/01 — 2018/8/31: Fingerprint Methods for Detection and Attribution of Changes in
Climate Extremes with Spatial Estimating Equations. $100,000. PI: Jun Yan.
7. NSF DMS1209022, 08/15/2012 — 07/31/2015: Statistical Inferences, Computing, and Applications for Semipara-
metric Accelerated Failure Time Models. $130,000. PI: Jun Yan. Co-PI: Sangwook Kang.
8. University of Wisconsin (NIH RO1 subcontract, PI: Hui-Chuan Lai), 09/01/2011 — 08/31/2016: Newborn Screening,
Malnutrition and Lung Disease in Children with Cystiv Fibrosis. $37,284. PI: Jun Yan.
9. NASA NNX10AG77G, 06/01/2010 — 05/31/2013: Testing the Suitability of Satellite Precipitation Products for Hy-
drological Modeling at Multiple Scales across the Blue Nile Basin. $308,845. PI: Mekonnen Gebremichael; Co-PI
Jun Yan.
10. NOAA NA10NWS4680004, 05/01/2010 — 04/30/2012: National Weather Service (NWS) Collaborative Science
Technology, and Applied Research (CSTAR) — A New Statistical Model of Streamflow Forecast Error. $148,574. PI:
Mekonnen Gebremichael; Co-PI Jun Yan.
11. NSF DMS0805965, 07/01/2008 — 06/30/2011: Unified Dynamic Modeling of Event Times with Semiparametric
Profile Estimating Functions: Theory, Computing, and Applications. $150,000. PI: Jun Yan.
12. NSF DMS0618883, 07/15/2006 — 07/14/2007: Statistical Computing Research Environments (SCREMS). $95,000.
PI: Mary Kathryn Cowles; Co-PIs: John Geweke, Jian Huang, Luke Tierney, and Jun Yan.
Internal
1. June 2024 – May 2025, The Connecticut Institute for the Brain and Cognitive Science, University of Connecticut —
Identifying the association between longitudinal changes in functional connectivity and Alzheimer’s disease progres-
sion.
2. January 2017 – December 2019, Innovative Education in Science, College of Liberal Arts and Sciences, University
of Connecticut — Data Science Lab: Real World Data Science Problems Meet Future Data Scientists (with Kun
Chen and Elizabeth Schifano).
3. April 2015 – March 2016, Research Excellence Program, University of Connecticut — Statistical Methods and Com-
puting for Detection and Attribution of Changes in Climate Extremes.
4. January 2011 – May 2011, Multidisciplinary Environmental Research Award, Center for Environmental Sciences
and Engineering, University of Connecticut — A Constrained Stochastic Model for Animal Movement Data with
Application to Deer Home Range (with Thomas Meyer).
5. January 2010 – December 2010, Faculty Large Grant, University of Connecticut — Semiparametric Methods for
Spatial Extremes with Application to Extremal Peak Flow in Connecticut.
6. January 2008 – December 2008, Faculty Large Grant, University of Connecticut — Partly Functional Temporal
Process Regression with Semiparametric Profile Estimating Functions: Theory and Application.
7. January 2008 – May 2008, Multidisciplinary Environmental Research Award, Center for Environmental Sciences and
Engineering, University of Connecticut — A Hierarchical Spatio-Temporal Model for Terrestrial Snails Abundances
in a Tropical Forest (with Michael Willig).
8. July 2006 – June 2007, Mathematical & Physical Sciences Funding Program (MPSFP), University of Iowa — Partly
Functional Temporal Process Regression.
9. January 2004 – December 2004, Mathematical & Physical Sciences Funding Program (MPSFP), University of Iowa
— Nonparametric Inference for Nonstationary Stochastic Processes.
Publications (*Student or postdoc supervisee)
Books and Edited Volumes
1. Hofert, M., Kojadinovic, I., M
¨
achler, M., and Yan, J. (2018): Elements of Copula Modeling with R. Springer.
2. Dey, D. K. and Yan, J. (eds.) (2015): Extreme Value Modeling and Risk Analysis: Methods and Applications.
Chapman & Hall/CRC.
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Book Chapters
1. Yin, F., *Jiao, J., Yan, J., and Hu, G.. (2022): Bayesian nonparametric estimation for point processes with spatial
homogeneity: A spatial analysis of NBA shot locations. Proceedings of the 39th International Conference on Machine
Learning. 162: 25523–25551.
2. *Vaughan, G., Aseltine, R., Chiou, S., and Yan, J. (2016): An alarm system for flu outbreaks using Google Flu
Trend data. In J. Lin, B. Wang, X. Hu, K. Chen, and R. Liu (eds.) Statistical Applications from Clinical Trials and
Personalized Medicine to Finance and Business Analytics, pp.293–304, Springer.
3. *Chiou, S., Kang, S., and Yan, J. (2015): Change point analysis of top baseball batting average. In D. K. Dey and
J. Yan (eds.) Extreme Value Modeling and Risk Analysis: Methods and Applications, pp.493–504, Chapman &
Hall/CRC.
4. Dey, D. K., Roy, D., and Yan, J. (2015): Univariate extreme value analysis. In D. K. Dey and J. Yan (eds.), Extreme
Value Modeling and Risk Analysis: Methods and Applications, pp.1–22, Chapman & Hall/CRC.
5. *Jiang, Y., Dey, D. K., and Yan, J. (2015): Multivariate extreme value analysis. In D. K. Dey and J. Yan (eds.), Extreme
Value Modeling and Risk Analysis: Methods and Applications, pp.23–39, Chapman & Hall/CRC.
6. *Wang, X., Sinha, A., Yan, J., and Chen, M.-H. (2012): Bayesian inference of interval-censored survival data. In
D.-G. Chen, J. Sun, and K. E. Peace (eds.), Interval-Censored Time-to-Event Data: Methods and Applications,
pp.167–196, Chapman & Hall/CRC.
7. Yan, J. (2006): Multivariate modeling with copulas and engineering applications. In H. Pham (ed.), Handbook of
Engineering Statistics, pp. 973–990, Springer.
Refereed Journal Articles
1. *Eada, S. T., Pozdnyakov, V., and Yan, J. (2024+): Discretely observed Brownian Motion governed by telegraph
signal process: Estimation and applications to finance. Statistical Inference for Stochastic Processes. Forthcoming.
2. *Jiao, J., Song, W., Xue, Y., and Yan, J. (2024+): Heteroscedastic growth curve modeling with shape-restricted
splines. New England Journal of Statistics in Data Science. Forthcoming.
3. Carter, E. J., *Lau, Y. T. A., Buchanan, L., Krol, D. M., Yan, J., and Aseltine, R. H. (2024+): Accountable care
organizations and HPV vaccine uptake: A multilevel analysis. American Journal of Managed Care. Forthcoming.
4. *Lautier, J., Pozdnyakov, V., and Yan, J. (2023+): Estimating a discrete distribution subject to random left-truncation
with an application to structured finance. Econometrics and Statistics. Forthcoming.
5. *Wang, W., Luo, C., Aseltine, R. H., Wang, F., Yan, J., and Chen, K. (2023+): Survival modeling of suicide risk with
rare and uncertain diagnoses. Statistics in Biosciences. Forthcoming.
6. *Chandy, M., Schifano, E. D., and Yan, J. (2024): On sample size needed for block bootstrap confidence intervals to
have desired coverage rates. American Journal of Undergraduate Research. 20(4): 3–16.
7. Choi, D., Bae, W., Yan, J., and Kang, S. (2024): A general model-checking procedure for semiparametric accelerated
failure time models. Statistics and Computing. 34(3): 117.
8. *Houlihan, K. and Yan, J. (2024): How to build a gymnastics team. Significance, 21(3), 10–14.
9. *Jin, J., Yan, J., Aseltine, R. H., and Chen, K. (2024) Transfer learning with quantile regression. Technometrics.
66(3): 381–393.
10. *Lautier, J., Pozdnyakov, V., and Yan, J. (2024): On the maximum likelihood estimation of a discrete, finite support
distribution under left-truncation and competing risks. Statistics and Probability Letters. 207: 109973.
11. *Yang, Z., Wang, H., and Yan, J. (2024): Optimal subsampling for semi-parametric accelerated failure time models
with massive survival data using a rank-based approach. Statistics in Medicine. 45(24): 4650–4666.
12. *Yang, Z., Wang, H., and Yan, J. (2024): Subsampling approach for least squares fitting of semi-parametric acceler-
ated failure time models to massive survival data. Statistics and Computing. 34(2): 77.
13. *Yuan, Y., Yan, J., and Zhang, P. (2024): A strength and sparsity preserving algorithm for generating weighted,
directed networks with predetermined assortativity. Physica A: Statistical Mechanics and Its Applications. 638:
129634.
14. *Zeimbekakis, A., Schifano, E. D., and Yan, J. (2024): On misuses of the Kolmogorov–Smirnov test for one-sample
goodness-of-fit. American Statistician. 78(4): 481–487.
10
15. Bar, H. and Yan, J. (2023): Legendary career and colorful life: A conversation with Dr. Bob Riffenburgh. Journal of
Data Science. 21(4): 818–837.
16. Chiou, S., Xu, G., Yan, J., and Huang, C.-Y. (2023): Regression modeling for recurrent events possibly with an
informative terminal event using R package reReg. Journal of Statistical Software. 105(1): 1–34.
17. *Lau, A. Y. Z, Wang, T., Yan, J., and Zhang, X. (2023): Extreme value modeling with errors-in-variables in detection
and attribution of changes in climate extremes. Statistics and Computing. 33(6): 125.
18. *Lautier, J. P., Pozdnyakov, P. and Yan, J. (2023): Pricing time-to-event contingent cash flows: A discrete-time
survival analysis approach. Insurance: Mathematics and Economics. 110: 53–71.
19. *Li, Y., Chen, K., Yan, J., and Zhang, X. (2023): Regularized fingerprinting in detection and attribution of climate
change with weight matrix optimizing the efficiency in scaling factor estimation. Annals of Applied Statistics. 17(1):
225–239.
20. *Ma, S., Wang, T., Yan, J., and Zhang, X. (2023): Optimal fingerprinting with estimating equations. Journal of
Climate. 36(20): 7109–7122.
21. *Yuan, Y., Wang, T., Yan, J., and Zhang, P. (2023): Generating general preferential attachment networks with R
package wdnet. Journal of Data Science. 21(3): 538–556.
22. *Wang, F., Wang, H,, and Yan, J. (2023): Diagnostic tests for the necessity of weight in regression with survey data.
International Statistical Review. 91(1): 55–71.
23. *Hu, C., Pozdnyakov, V., and Yan, J. (2022): On occupation time for on-off processes with multiple off-states. Modern
Stochastics: Theory and Applications. 9(4): 413–430.
24. *Jiao, J., Tang, Z., Yue, M., Zhang, P., and Yan, J. (2022): Cyberattack-resilient load forecasting with adaptive robust
regression. International Journal of Forecasting. 38(3): 910–919.
25. *Lau, A. Y. Z and Yan, J. (2022): Bias analysis of generalized estimating equations under measurement error and
practical bias correction. Stat. 11(1): e418.
26. *Price, M. and Yan, J. (2022): The effects of the NBA COVID Bubble on the NBA playoffs: A case study for home-
court advantage. American Journal of Undergraduate Research. 18(4): 3–15.
27. Sun, Q., Zwiers, F., Zhang, X., and Yan, J. (2022): Quantifying the human influence on the intensity of extreme 1-
and 5-day precipitation amounts at global, continental, and regional scales. Journal of Climate. 35(1): 195–210.
28. Wang, T., Yan, J., *Yuan, Y., and Zhang, P. (2022): Generating directed networks with predetermined assortativity
measures. Statistics and Computing. 32: 91.
29. *Xiao, S., Yan, J., and Zhang, P. (2022): Incorporating auxiliary information in betweeness measure for input-output
networks. Physica A: Statistical Mechanics and Its Applications. 607: 128200.
30. *Yang, Z., Wang, H., and Yan, J. (2022): Optimal subsampling for parametric accelerated failure time models with
massive survival data. Statistics in Medicine. 41(27): 5241–5431.
31. Zhang, P., Wang, T., and Yan, J. (2022): PageRank centrality and algorithms for weighted, directed networks.
Physica A: Statistical Mechanics and Its Applications. 586: 126438.
32. Chang, S.-Y., Jin, J., Yan, J., Dong, X., Chaudhuri, B., Nagapudi, K, and Ma, A. W. K. (2021): Development of a
pilot-scale HuskyJet binder jet 3D printer for additive manufacturing of pharmaceutical tablets. International Journal
of Pharmaceutics. 605: 120791.
33. Feng Chang, C., Garcia, V., Tang, C., Vlahos, P., Wanik, D., Yan, J., Bash, J. O., and Astitha, M. (2021): Linking
multi-media modeling with machine learning to assess and predict lake chlorophyll-α concentrations. Journal of
Great Lakes Research. 47(6): 1656–1670.
34. *Hu, C., Elbroach, M., Meyer, T., Pozdnyakov, V., and Yan, J. (2021): Moving-resting process with measurement
error in animal movement modeling. Methods in Ecology and Evolution. 12(11): 2221–2233.
35. *Jiao, J., Hu, G., and Yan, J. (2021): A Bayesian marked spatial point processes for basketball shot chart. Journal
of Quantitative Analysis in Sports. 17(2): 77–90.
36. *Jiao, J., Hu, G., and Yan, J. (2021): Heterogeneity pursuit for spatial point pattern with application to tree locations:
A Bayesian semiparametric recourse. Environmetrics. 32(7): e2694.
37. *Li, Y., Chen, K., Yan, J., and Zhang, X. (2021): Uncertainty in optimal fingerprinting is underestimated. Environ-
mental Research Letters. 16(8): 084043.
11
38. *Wang, T., Xiao, S., Yan, J., and Zhang, P. (2021): Regional and sectoral structures of the Chinese economy: A
network perspective from multi-regional input-output tables. Physica A: Statistical Mechanics and Its Applications.
581: 126196.
39. *Wang, W. and Yan, J. (2021): Shape-restricted regression splines with R package splines2. Journal of Data Sci-
ence. 19(3): 498–517.
40. *Wang, Z., Jiang, Y., Wan, H., Yan, J., and Zhang, X. (2021): Towards optimal fingerprinting in detection and
attribution of changes in climate extremes. Journal of the American Statistical Association. 116(553): 1–13.
41. Wang, T., and Yan, J. (2021): Discussion of “On studying extreme values and systematic risks with nonlinear time
series models and tail dependence measures”. Statistical Theory and Related Fields. 5(1): 38–40.
42. *Wu, J., Chen, M.-H., Schifano, E. D., and Yan, J. (2021): Online updating of survival analysis. Journal of Computa-
tional and Graphical Statistics. 30(4): 1209–1223.
43. *Xue, Y., Yan, J., and Schifano, E. D. (2021): Simultaneous monitoring for regression coefficients and baseline
hazard profile in Cox modeling of time-to-event data. Biostatistics. 22(4): 756–771.
44. *Yuan, Y., Yan, J., Zhang, P. (2021): Assortativity measures for weighted and directed networks. Journal of Complex
Networks. 9(2): cnab017.
45. *Doshi, R., Yan, J., and Aseltine, R. (2020): Age differences in racial/ethnic disparities in preventable hospitalizations
for heart failure in Connecticut, 2009-2015: A population-based longitudinal study. Public Health Reports 135(1):
56–65.
46. *Hu, C., Pozdnyakov, V., and Yan, J. (2020): Density and distribution evaluation for convolution of independent
gamma variables. Computational Statistics. 35(1): 327–342.
47. *Jiang, Y., He, X., Lee, M-L. T., Rosner, B., and Yan, J. (2020): Rank-based tests for clustered data with R package
clusrank. Journal of Statistical Software. 96(6): 1–26.
48. *Li, Y., Li, Y., Qin, Y., and Yan, J. (2020): Copula modeling for data with ties. Statistics and Its Interfaces. 13(1):
103–117.
49. Pozdnyakov, V., Elbroach, L. M., Hu, C., Meyer, T., and Yan, J. (2020): On estimation for Brownian motion governed
by telegraph process with multiple off states. Methodology and Computing in Applied Probability 22: 1275–1291.
50. *Vaughan, G., Aseltine, R., Chen, K., and Yan, J. (2020): Efficient interaction selection for clustered data via stage-
wise generalized estimating equations. Statistics in Medicine. 39(22): 2855–2868.
51. *Wang, W., Aseltine, R., Chen, K., and Yan, J. (2020): Integrative survival analysis with uncertain event times in
application to a suicide risk study. Annals of Applied Statistics. 14(1): 51-73.
52. *Wang, C., Schifano, E. D., and Yan, J. (2020): Geographic ratings with spatial random effects in a two-part model.
Variance. 13(1): 141–160.
53. Xu, G., Chiou, S., Yan, J., Marr, K., and Huang, C.-Y. (2020): Generalized scale-change models for recurrent event
processes under informative censoring. Statistica Sinica. 30(4): 1773–1795.
54. *Xue, Y., Wang, H., Yan, J., and Schifano, E. D. (2020), An online updating approach for testing the proportional
hazards assumption with streams survival data. Biometrics. 76(1): 171–182.
55. Aseltine, R., *Wang, W., Benthien, R., Katz, M., Wagner, C., Yan, J., and Lewis, C. (2019): Reductions in race and
ethnic disparities in hospital readmissions following total joint arthroplasty from 2005–2015. Journal of Bone and
Joint Surgery. 101(22) 2044–2050.
56. Caplan, D. J., *Li, Y., *Wang, W., Kang, S., Marchini, L., Cowen, H. J., and Yan, J. (2019): Dental restoration longevity
among geriatric and special needs patients. JDR Clinical & Translational Research. 4(1): 41–48.
57. Chiou, S., Huang, C., Xu, G., and Yan, J. (2019): Semiparametric regression analysis of panel count data: A practical
review. International Statistical Review. 87(1), 24–43.
58. Pozdnyakov, V., Elbroch, M., Labarga, J. A., Meyer, T., and Yan, J. (2019): Discretely observed Brownian motion
governed by telegraph process: Estimation. Methodology & Computing in Applied Probability 21(3): 907–920.
59. *Bader, B., Yan, J., and Zhang, X. (2018): Automated threshold selection for extreme value analysis via goodness-
of-fit tests with application to return level mapping. Annals of Applied Statistics 12(1): 310–329.
60. Chiou, S., Xu, G., Yan, J., and Huang, C. (2018): Semiparametric estimation of the accelerated mean model for
panel count data with informative examination times. Biometrics, 74(3): 944–953.
12
61. *Wang, C., Chen, M.-H., Wu, J., Yan, J., Zhang, Y., and Schifano, E. D. (2018): Online updating method with new
variables for big data streams. Canadian Journal of Statistics 46(1): 123–146.
62. *Bader, B., Yan, J., and Zhang, X. (2017): Automated selection of r for the r largest order statistics approach with
adjustment for sequential testing. Statistics and Computing 27(6): 1435–1451.
63. *Vaughan, G., Aseltine, R. H, Chen, K., and Yan, J. (2017): Stagewise estimating equations with grouped variables.
Biometrics 73(4): 1332–1342.
64. *Wang, Z., Jiang, Y., Wan, H., Yan, J., and Zhang, X. (2017): Detection and attribution of changes in extreme
temperatures at regional level. Journal of Climate 30(17): 7035–7047.
65. Xu, G., Chiou, S., Huang, C.-Y., Wang, M.-C., and Yan, J. (2017): Joint scale-change models for recurrent events
and failure time. Journal of the American Statistical Association 112: 794–805.
66. Olayivola, J. N., Adnerson, D. R., Jepeal, N., Aseltine, R. H., Pickett, C., Yan, J., and Zlateva, I. (2016): Electronic
consultations to improve the primary care-specialty care interface for cardiology in the medically underserved: A
cluster-randomized controlled trial. Annals of Family Medicine 14(2): 133–140.
67. Schifano, E. D., Wu, J., Wang, C., Yan, J. and Chen, M.-H. (2016): Online updating of statistical inference in the big
data setting. Technometrics 58(3): 393–403.
68. *Wang, C., Chen, M.-H., Schifano, E. D., Wu, J., and Yan, J. (2016): Statistical methods and computing for big data.
Statistics and Its Interfaces 9(4): 399–414.
69. *Wang, W., Chen, M.-H., Chiou, S., Lai, H.-C., Wang, X., Yan, J., and Zhang, Z. (2016): Onset of persistent Pseu-
domonas Aeruginosa infection in children with cystic fibrosis with interval censored data. BMC Medical Research
Methodology 16(122): 1–10.
70. Aseltine, R. H., Yan, J., Fleischman, S., Katz, M., and DeFrancesco M. (2015): Race and ethnic disparities in hospital
readmissions following vaginal and cesarean delivery. Obstetrics & Gynecology 126(5): 1040–1047.
71. Aseltine, R. H., Yan, J., Gruss, C. B., Wagner, C., and Katz, M. (2015): Connecticut hospital readmissions related to
chest pain and heart failure: Differences by race, ethnic, and payer. Connecticut Medicine 79(2): 69–76.
72. Chi, Z., Pozdnyakov, V., and Yan, J. (2015): On occupation time of Brownian motion. Statistics and Probability
Letters 97: 83–87.
73. *Chiou, S., Kang, S., and Yan, J. (2015): Semiparametric accelerated failure time modeling for clustered failure times
from stratified sampling. Journal of the American Statistical Association 110: 621–629.
74. *Chiou, S., Kang, S., and Yan, J. (2015): Rank-based estimating equations with general weight for accelerated failure
time models: An induced smoothing approach. Statistics in Medicine 34(9): 1495–1510.
75. Kojadinovic, I., Shang, H., and Yan, J. (2015): A class of goodness-of-fit tests for spatial extremes models based on
max-stable processes. Statistics and Its Interfaces 8(1): 45–62.
76. *Prates, M. O., Dey, D. K., Willig, M. R., and Yan, J. (2015): Transformed Gaussian Markov random fields and spatial
modeling. Spatial Statistics 14(C), 382–399.
77. *Shang, H., Yan, J., and Zhang, X. (2015): A two-step approach to model precipitation extremes in California based
on max-stable and marginal point processes. The Annals of Applied Statistics 9(1): 452–473.
78. *Chiou, S., Kang, S., Kim, J., and Yan, J. (2014): Marginal semiparametric multivariate accelerated failure time model
with generalized estimating equations. Lifetime Data Analysis 20(4): 599–618.
79. *Chiou, S., Kang, S., and Yan, J. (2014): Fitting accelerated failure time models in routine survival analysis. Journal
of Statistical Software 61(11): 1–23.
80. *Chiou, S., Kang, S., and Yan, J. (2014): Fast accelerated failure time modeling for case-cohort data. Statistics and
Computing 24(4): 559–568.
81. Pozdnyakov, V., Meyer, T., Wang, Y., and Yan, J. (2014): On modeling animal movement using Brownian motion with
measurement error. Ecology 95(2): 247–253.
82. *Wang, Z., Yan, J., and Zhang, X. (2014): Incorporating spatial dependence in regional frequency analysis. Water
Resources Research 50(12): 9570–9585.
83. Yan, J., Chen, Y., Lawrence-Apfel, K., Ortega, I. M., Pozdnyakov, V., Williams, S., and Meyer, T. (2014): A moving-
resting process with an embedded Brownian motion for animal movements. Population Ecology 56(2): 401–415.
13
84. Yan, J., Guo, C., Paarlberg, L. E. (2014): Are antipoverty nonprofit organizations located where they are needed?
Spatial analysis of the Greater Hartford region. American Statistician 68(4): 243–252.
85. *Prates, M. O., Aseltine, R. H., Dey, D. K., and Yan, J. (2013): Assessing intervention efficacy on high risk drinkers
using generalized linear mixed models with a new class of link functions. Biometrical Journal 55(6): 912–924.
86. *Wang, X., Chen, M.-H., and Yan, J. (2013): Bayesian dynamic regression model for interval censored data. Lifetime
Data Analysis 19(3): 297–316.
87. *Wang, X., Ma, S., and Yan, J. (2013): Augmented estimating equations for semiparametric panel count regression
with informative observation times and censoring time. Statistica Sinica 23(1): 359–381.
88. *Wang, X. and Yan, J. (2013): Practical notes on multivariate modeling based on elliptical copulas. Journal of the
French Statistical Society 154(1): 102–115.
89. Yan, J., Aseltine, R., and Harel, O. (2013): Comparing regression coefficients between nested models for clustered
data with generalized estimating equations. Journal of Educational and Behavioral Statistics 38(2): 172–189.
90. Cavallo, A., Rosenthal, B., Wang, X., and Yan, J. (2012): Treatment of the data collection threshold in operational
risk: A case study using the lognormal distribution. Journal of Operational Risk 7(1): 3–38.
91. Chen, D. C. R., Kirshenbaum, D. S., Yan, J., Kirshenbaum, E., and Aseltine, R. H. (2012): Characterizing changes
in student empathy throughout medical school. Medical Teacher 34(4): 305––311.
92. Havens, E. K., Martin, K. S., Yan, J., Dauser-Forrest, D., and Ferris, A. M. (2012): Federal nutrition program changes
and healthy food availability. American Journal of Preventive Medicine 43(4): 419–422.
93. Kojadinovic, I. and Yan, J. (2012): A nonparametric test of exchangeability for bivariate extreme-value copulas.
Scandinavian Journal of Statistics 39(3): 480–496.
94. Kojadinovic, I. and Yan, J. (2012): Goodness-of-fit testing based on a weighted bootstrap: A fast large-sample
alternative to the parametric bootstrap. Canadian Journal of Statistics 40(3): 480–500.
95. Yan, J. and Huang, J. (2012): Model selection for time-varying coefficient Cox models. Biometrics 68(2): 419–428.
96. Yan, J., Liao, G.-Y., Gebremichael, M., Shedd, R., and Vallee, D. (2012): Characterizing the Uncertainty in River
Stage Forecasts Conditional on Point Forecast Values. Water Resources Research 48: W12509.
97. Allignol, A., Latouche, A., Yan, J., and Fine, J. P. (2011): A regression model for the conditional probability of
a competing event: Application to monoclonal gammopathy of unknown significance. Journal of Royal Statistical
Society, Series C: Applied Statistics 60(1): 135–142.
98. Gebremichael, M., Liao, G.-Y., and Yan, J. (2011): Non-parametric error model for high resolution satellite rainfall
products. Water Resources Research 47: W07504–W07512.
99. Genest C., Kojadinovic I., Ne
ˇ
slehov
´
a J., and Yan J. (2011): A goodness-of-fit test for bivariate extreme value copulas.
Bernoulli 17(1): 253-275.
100. Guan, Y., Yan, J., and Sinha, R. (2011): Variance estimation for statistics computed from single recurrent event
processes. Biometrics 17(3): 711–718.
101. Harel, O., Mukhopadhyay, N., and Yan, J. (2011): On a sequential probability ratio rest subject to incomplete data.
Sequential Analysis 30: 441–456.
102. Kojadinovic, I., Segers, J., and Yan, J. (2011): Large-sample tests of extreme-value dependence for multivariate
copulas. Canadian Journal of Statistics 39(4): 703–720.
103. Kojadinovic, I. and Yan, J. (2011): Tests of serial independence for multivariate time series based on a M
¨
obius
decomposition of the independence empirical copula process. Annals of the Institute of Statistical Mathematics
63(2): 347–373.
104. Kojadinovic, I. and Yan, J. (2011): A goodness-of-fit test for multivariate multiparameter copulas based on multiplier
central limit theorems. Statistics and Computing 21(1): 17–30.
105. Kojadinovic, I, Yan, J., and Holmes, M. (2011): Fast large-sample goodness-of-fit for copulas. Statistica Sinica 21(2):
841–871.
106. *Prates, M. O., Dey, D. K, Willig, M. R. and Yan, J. (2011): Intervention analysis of the hurricane impact on snail
abundance in a tropical forest with spatio-temporal data. Journal of Agricultural, Biological, and Ecological Statistics
16(1): 142–156.
14
107. *Shang, H., Yan, J., Gebremichael, M., and Ayalew, S. M. (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(3): 1937–1944.
108. *Shang, H., Yan, J., and Zhang, X. (2011): ENSO influence on winter maximum daily precipitation in California in a
spatial model. Water Resources Research 47: W11507–W11515.
109. *Wang, X. and Yan, J. (2011): Fitting semiparametric regressions for panel count survival data with an R package
spef. Computer Methods and Programs in Biomedicine 104(2): 278–285.
110. Kojadinovic, I. and Yan, J. (2010): Nonparametric rank-based tests of bivariate extreme-value dependence. Journal
of Multivariate Analysis 101(9): 2234–2249.
111. Kojadinovic, I. and Yan, J. (2010): Comparison of three semiparametric methods for estimating dependence param-
eters in copula models. Insurance: Mathematics and Economics 47(1): 52–63.
112. Kojadinovic, I. and Yan, J. (2010): Modeling multivariate distributions with continuous margins using the copula R
package. Journal of Statistical Software 34(9): 1–20.
113. *Wang, X., Gebremichael, M., and Yan, J. (2010): Weighted likelihood copula modeling of extreme rainfall events in
Connecticut. Journal of Hydrology 390(1–2): 108–115.
114. Yan, J. and the Academic ED SBIRT Research Collaborative. (2010): The impact of screening, brief intervention
and referral for treatment in emergency department patients’ alcohol use: A 3-, 6- and 12-month follow-up. Alcohol
& Alcoholism 45(6): 514–519.
115. Yan, J., Cheng, Y., Fine, J. P., and Lai, H.-C. (2010): Uncovering symptom progression history from disease registry
data with application to young cystic fibrosis patients. Biometrics 66(2): 594–602.
116. Cowles, M. K., Yan, J., and Smith, B. J. (2009): Reparameterized and marginalized posterior and predictive sampling
for complex Bayesian geostatistical models. Journal of Computational and Graphical Statistics 18(2): 262–282.
117. Yan, J. and Gebremichael, M. (2009): Estimating actual rainfall from satellite rainfall products. Atmospheric Research
92(4): 481–488.
118. Yan, J. and Huang, J. (2009): Partly functional temporal process regression with semiparametric profile estimating
functions. Biometrics 65(2): 431–440.
119. Smith, B. J., Yan, J., and Cowles, M. K. (2008): Unified geostatistical modeling for data fusion and spatial het-
eroskedasticity with R package ramps. Journal of Statistical Software 25(10): 1–21.
120. Yan, J. and Fine, J. P. (2008): Analysis of episodic data with application to recurrent pulmonary exacerbations in
cystic fibrosis patients. Journal of the American Statistical Association 103: 498–510.
121. Stramer, O. and Yan, J. (2007): Asymptotics of an efficient Monte Carlo estimation for the transition density of
diffusion processes. Methodology & Computing in Applied Probability 9(4): 483–496.
122. Stramer, O. and Yan, J. (2007): On simulated likelihood of discretely observed diffusion processes and comparison
to closed-form approximation. Journal of Computational and Graphical Statistics 16(3): 672–691.
123. Yan, J. (2007): Enjoy the joy of copulas. Journal of Statistical Software 21(4): 1–21.
124. Yan, J. (2007): Spatial stochastic volatility for lattice Data. Journal of Agricultural, Biological, and Environmental
Statistics 12(1): 25–40.
125. Yan, J., Cowles, M. K., Wang, S., and Armstrong, M. P. (2007): Parallelizing MCMC for Bayesian spatiotemporal
geostatistical models. Statistics and Computing 17(4): 323–335.
126. Yan, J. and Tamboli, C. P. (2007): Testing concordance of clinical characteristics in familial studies with application
to inflammatory bowel diseases. Biometrical Journal 49(6): 840–853.
127. Halekoh, U., Højsgaard, S., and Yan, J. (2006): The R package geepack for generalized estimating equations.
Journal of Statistical Software 15(2): 1–11.
128. Yan, J. and Fine, J. P. (2005): Functional association models for multivariate survival processes. Journal of the
American Statistical Association 100(469): 184–196.
129. Fine, J. P., Yan, J., and Kosorok, M. R. (2004): Temporal process regression. Biometrika 91(3): 683–703.
130. Yan, J. and Fine, J. P. (2004): Estimating equations for association structures (Pkg: P859-880). Statistics in Medicine
23(6): 859–874.
15
131. Yan, J. and Fine, J. P. (2004): Reply to comment on “Estimating equations for association structures” (Pkg: 859–880).
Statistics in Medicine 23(6): 879–880.
Papers Submitted to Refereed Journals
1. *Godoy, L., Prates, M., and Yan, J. (2024): Statistical inferences and predictions for areal data and spatial data fusion
with Hausdorff-–Gaussian processes.
2. *Louit, S., Clark, E., Gelbard, Al, Vivek, N., Yan, J., and Zhang, P. (2024): CALF-SBM: A covariate-assisted latent
factor stochastic block model.
3. *Xu, Z., Fine, J. P., Song, W., and Yan, J. (2024): On GEE for mean-variance-correlation models: Variance estimation
and model selection.
4. Shen, O., Feng, Q., Yan, J., and Zhang, P. (2024): Rank-based assortativity for weighted, directed networks.
5. *Hughes, S., Matthews, G., and Yan, J. (2024): Statistical evaluation of outdoor field hockey penalty corners.
6. *Fiore, O., Schifano, E. D., and Yan, J. (2023): On Devon Allen’s disqualification at the 2022 World Track and Field
Championships.
7. *Wang, T., *Xiao, S., and Yan, J. (2023): Comparison of sectoral structures between China and Japan: A network
perspective.
8. Chiou, S., Aseltine, R., Schilling, E., Lutz, K., and Yan, J. (2022): A bivariate two-part model for censored durations
of depression and relational stressor in young adults.
9. *Lautier, J., Pozdnyakov, V., and Yan, J. (2022): On the convergence of credit risk in current consumer automobile
loans.
10. *Xie, Y., Fu, H., Huang, Y., Pozdnyakov, V., and Yan, J. (2022): Recurrent events modeling based on a reflected
Brownian motion with application to hypoglycemia.
11. *Godoy, L., Prates, M., and Yan, J. (2022): Model-based Voronoi linkage between point-referenced data and areal
data in spatial analysis with application to Brazilian election 2018.
12. *Ma, S., Yan, J., and Zhang, X. (2020): Extreme value modeling with generalized Pareto distributions for rounded
data.
Software
1. *Bader, B. and Yan, J.: R package eva on CRAN, extreme value analysis.
2. *Chiou, S., Kang, S., and Yan, J.: R package aftgee on CRAN, multivariate accelerated failure time modeling with
generalized estimating equations.
3. *Chiou, S., Wang, X. and Yan, J: R package spef on CRAN, semiparametric estimating functions.
4. Hofert, M., Kojadinovic, I., M
¨
achler, M. and Yan, J.: R package copula on CRAN, multivariate dependence with
copula.
5. *Hu, C., Pozdnyakov, V., and Yan, J.: R package coga on CRAN, convolution of gamma variables.
6. *Hu, C., Yan, J., and Pozdnyakov, V.: R package smam on CRAN, statistical modeling of animal movement.
7. *Jiang, Y., Lee, M.-L. T., and Yan, J.: R package clusrank on CRAN, rank-based tests for clustered data.
8. Kojadinovic, I. and Yan, J.: R package fgof on CRAN, fast goodness-of-fit test.
9. *Li, Y., Chen, K., and Yan, J.: R package tls on CRAN, total least squares.
10. *Li, Y., Chen, K., and Yan, J.: R package dacc on CRAN, detection and attribution of climate change.
11. *Li, Y., Wang, W., and Yan, J.: R package touch on CRAN, tools of utilization and cost in healthcare.
12. *Prates, M. O., Wang, W., and Yan, J.: R package rbugs on CRAN, fusing R with OpenBugs.
13. Smith, B. P., Yan, J., and Cowles, M. K.: R package ramps on CRAN, reparametrized and marginalized posterior
sampling.
14. *Vaughan, G., Chen, K., and Yan, J.: R package sgee on CRAN, stagewise generalized estimating equations.
15. *Wang, W., Chen, K., and Yan, J.: R package intsurv on CRAN, integrative survival analysis.
16. *Wang, W., Fu, H., and Yan, J.: R package reda on CRAN, recurrent event data analysis.
16
17. *Wang, W. and Yan, J.: R package splines2 on CRAN, regression spline functions and classes too.
18. *Wang, X., Chen, M.-H., and Yan, J.: R package dynsurv on CRAN, dynamic survival modeling.
19. *Xiao, S., Yan, J., and Zhang, P.: R package fcstat on GitHub, statistical methods for estimating functional con-
nectivity analysis in brain networks.
20. *Xiao, S., Yan, J., and Zhang, P.: R package ionet on CRAN, input-output networks.
21. Yan, J.: R package tpr on CRAN, temporal process regression.
22. Yan, J.: R package som on CRAN, self-organizing map with application to gene clustering.
23. Yan, J., Højsgaard, S., and Halekoh, U.: R package geepack on CRAN, generalized estimating equation package.
24. Yan, J.: R package KMsurv on CRAN, datasets and functions for Klein and Moeschberger (1997), “Survival Analysis,
Techniques for Censored and Truncated Data”, Springer.
25. *Yuan, Y., Wang, T., Yan, J., and Zhang, P.: R package wdnet on CRAN, weighted directed networks.
Non-Refereed Publications
1. Yan, J. (2020): A reformed Journal of Data Science for the era of data science. Journal of Data Science, 18(3):
405–406.
2. Follman, D., Song, P. X.-K., Wang, H., and Yan, J. (2020): Data science in action in response to the outbreak of
COVID-19. Journal of Data Science 18(3): 407–408.
3. Yan, J. (2004): Fusing R and BUGS through Wine. R News 4(2): 19–21.
4. Yan, J. and Rossini, A. (2003): Building Microsoft Windows versions of R and R packages under Intel Linux. R News
3(1): 15–17.
5. Yan, J. (2002): geepack: Yet another package for generalized estimating equations. R News 2(3): 12–14.
Book Reviews
1. Yan, J. (2006): Gaussian Markov random fields: Theory and applications. Harvard Rue and Leonhard Held. Journal
of the American Statistical Association 101(473): 388–389.
2. Yan, J. (2005): Analysis of multivariate survival data. Phillip Hougaard. Journal of the American Statistical Associa-
tion 100(469): 355–356.
3. Yan, J. (2004): Bayesian survival analysis. Joseph G. Ibrahim, Ming-Hui Chen, and Debajyoti Sinha. Journal of the
American Statistical Association 99(468): 1202–1203.
4. Yan, J. (2004): Survival analysis: Techniques for censored and truncated data (2nd ed.). John P. Klein and Melvin
L. Moeschberger. Journal of the American Statistical Association 99(467): 900–901.
Invited Talks
Recurrent Events Modeling Based on a Reflected Brownian Motion with Application to Hypoglycemia, 05/23/2024, New
England Statistics Symposium 2024.
Recurrent Events Modeling Based on a Reflected Brownian Motion with Application to Hypoglycemia, 04/18/2024, Depart-
ment of Biostatistics, University of Pittsburgh.
Optimal Fingerprinting in Climate Change Detection and Attribution with Estimating Equations, 11/20/2023, Department of
Statistics, Oregon State University (virtual).
Introduction to Survival Analysis, 10/17/2023, Department of Statistics, University of Lagos, Nigeria (Virtual).
Recurrent Events Modeling Based on a Reflected Brownian Motion with Application to Hypoglycemia, 03/30/2023, Depart-
ment of Statistics, Kansas State University (virtual).
Optimal Fingerprinting with Estimating-Equations, 08/22/2022, Learning the Earth with Artificial Intelligence and Physics
Center, Columbia University.
Introductory Overview Lecture: Sports Analytics Beyond Performance Evaluation, 08/08/2022, Joint Statistical Meetings.
Optimal Fingerprinting with Bias-Corrected Estimating Equations, 06/11/2022, Special Conference Celebrating the 80th
Anniversary of Renmin, Institute of Statistics and Big Data, Renmin University of China (virtual).
17
Recurrent Events Modeling Based on a Reflected Brownian Motion with Application to Hypoglycemia, 05/18/2022, Depart-
ment of Applied Mathematics, The Hong Kong Polytechnic University (virtual).
Recurrent Events Modeling Based on a Reflected Brownian Motion with Application to Hypoglycemia, 03/29/2022, ENAR
Spring Meeting (virtual).
Optimal Fingerprinting with Bias-Corrected Estimating Equations, 01/31/2022, International Detection and Attribution Group
(virtual).
Brownian Motion Governed by Telegraph Process in Modeling High-Frequency Financial Series, 08/27/2021, Department
of Statistics, Universidade Federal de Minas Gerais (virtual).
Correct Working Correlation for Generalized Estimating Equations May Lead to More Bias Under Measurement Error than
Working Independence, 06/09/2021, Department of Applied Statistics, Yonsei University (virtual).
Moving-Resting Process with Measurement Error in Animal Movement Modeling, 10/26/2020, Center for Statistical Sci-
ence, Tsinghua University (virtual).
An Applied Statistician’s Adventure: Climate Change, Animal Movement, Sports Analytics, and Beyond, 05/09/2020,
Clubear Lecture (virtual).
An Online Updating Approach for Testing the Proportional Hazards Assumption with Streams of Survival Data, 11/29/2019,
The 6th Workshop on Survival Analysis and Applications, University of San Paulo, Brazil.
Integrative Survival Analysis with Uncertain Event Times in Application to a Suicide Risk Study, 07/05/2019, School of
Statistics, Shanxi University of Finance and Economics.
Acrobatic Regression in Detection and Attribution of Climate Change, 07/03/2019, Guanghua School of Management,
Peking University.
An Online Updating Approach for Testing the Proportional Hazards Assumption with Streams of Survival Data, 06/30/2019,
School of Mathematics, Jilin University
Fingerprinting Changes in Climate Extremes with Joint Modeling of Observations and Climate Model Simulation, 07/30/2018,
Vancouver, BC, Canada, Joint Statistical Meetings.
Generalized Scale-Change Models for Recurrent Event Processes under Informative Censoring, 07/03/2018, 2018 ICSA
China Conference with the Focus on Data Science, Qingdao, China.
Growth Curve Analysis with Shape-restricted Splines, 07/01/2018, International Statistics Forum, Renmin University of
China, Beijing, China.
Generalized Scale-Change Models for Recurrent Event Processes under Informative Censoring, 06/30/2018, School of
Mathematics, Jilin University.
Optimal Fingerprinting in Detection and Attribution of Changes in Climate Extremes, 06/26/2018, Center for Statistical
Science, Peking University.
Statistical Methods for Big Stream Data, 06/19/2018, Shanxi University of Finance and Economics
Things about Being a Professor, 12/20/2017, School of Statistics, Renmin University of China.
Online Updating Method with New Variables for Big Data Streams, 12/17/2017, School of Statistics, Shanxi University of
Finance and Economics.
Balancing the Bias-Variance Tradeoff in Extreme Value Analysis, 08/31/2017, Center for Mathematical Research, University
of Montr
´
eal.
Stagewise Generalized Estimating Equations with Grouped Variables, 03/02/2017, Department of Mathematics and Statis-
tics, Boston University.
Online Updating Method with New Variables for Big Data Streams, 10/12/2016, Department of Statistics and Biostatistics,
Rutgers University.
Semiparametric Accelerated Failure Time Modeling for Clustered Failure Times From Stratified Sampling, 07/02/2016,
IBS-China 4th International Biostatistics Symposium, Shanghai, China.
Spatial estimating equations for detection and attribution of changes in climate extremes, 05/29/2016, China R Conference,
Beijing, China.
Spatial estimating equations for detection and attribution of changes in climate extremes, 02/02/2016, International Detec-
tion and Attribution Group Meeting, Boulder, CO.
18
Spatial estimating equations with application to changes in climate extremes, 10/26/2015, Department of Mathematical
Sciences, Worcester Polytechnic Institute.
Optimal fingerprinting in detection and attribution of changes in climate extremes with combined score equations, 06/20/2015,
Alumni Symposium, School of Statistics, Renmin University of China, Beijing, China.
Onset time of chronic pseudomonas aeruginosa infection of cystic fibrosis patients with interval censored data, 06/16/2015,
Applied Statistics Symposium, International Chinese Statistical Association, Fort Collins, CO.
A bivariate two-part model to assess the effect of coping strategy on stressor and depression, 06/06/2015, Frontiers in
Applied and Computational Mathematics 2015, New Jersey Institute of Technology (NJIT) in Newark, New Jersey.
Incorporating spatial dependence in regional frequency analysis, 05/25/2014, International Statistics Forum, Renmin Uni-
versity of China, Beijing China.
A partial review of software for big data statistics, 02/12/2014, Statistical and Computational Theory and Methodology for
Big Data Analysis, Banff International Research Station, Banff, Alberta, Canada.
Statistics methods and computing for semiparametric accelerated failure time models with induced smoothing, 05/13/2013,
Department of Biostatistics, Brown University.
Transformed Gaussian Markov random fields and Spatial Modeling, 11/23/2012, Universitdade Federal de Minas Gerais,
Brazil.
Transformed Gaussian Markov random fields and Spatial Modeling, 10/05/2012, Department of Biostatistics, University of
Massachusetts—Amherst.
Fast accelerated failure time model for case-cohort data, 06/24/2012, Boston, MA, International Chinese Statistical Asso-
ciation Applied Statistics Symposium.
Model selection for Cox models with time-varying coefficients, 04/21/2012, Boston University, New England Statistics
Symposium.
Max-Stable processes for spatial extremes modeling: A review and some ongoing research, 03/01/2012, Climate Research
Division, Environmental Canada.
Multivariate accelerated failure time models with generalized estimating equations, 10/04/2011, Biostatistics Research
Branch, National Institute of Allergy and Infectious Disease.
Augmented estimating equations for semiparametric panel count regression with informative censoring, 03/23/2011, ENAR
Spring Meeting.
Nonparametric rank-based tests of bivariate extreme-value dependence, 10/27/2010, UMass-UConn Joint Statistics Sym-
posium.
Augmented estimating equations for semiparametric panel count regression with informative censoring, 06/30/2010, Yun-
nan University, International Conference on Statistical Analysis of Complex Data.
Nonparametric rank-based tests of bivariate extreme-value dependence, 04/17/2010, Harvard University, 2010 New Eng-
land Statistics Symposium.
Combining data for efficient prediction of the spatial distribution of Iowa residential radon levels, 08/02/2009, Washington,
DC, Joint Statistical Meetings.
Fast large sample goodness-of-fit test for copulas, 10/02/2008, D
´
epartement de math
´
ematiques et de statistique, Universit
´
e
Laval.
Tests of serial independence for multivariate time series based on a M
¨
obius decomposition of the independence empirical
copula process, 06/21/2008, Renmin University of China, International Statistics Forum 2008.
Partly functional temporal process regression with semiparametric profile estimating functions, 01/29/2008, Division of
Biostatistics, Yale University.
Spatial stochastic volatility, 07/12/2006, Beijing, China, Far Eastern Meeting of the Econometrics Society (FEMES) 2006.
Partly functional temporal process regression, 06/28/2006, Hong Kong, China, INFORMS International Conference 2006.
Spatial stochastic volatility, 04/21/2006, Department of Economics, University of Illinois – Urbana-Champaign.
Temporal process regression, 08/10/2004, Toronto, Canada, Joint Statistical Meeting.
Invited Workshops/Shortcourses
19
June 2024, Climate Change Detection and Attribution with Estimating Equations (2-hour short course with Yan Li), 15th
International Meeting on Statistical Climatology, Toulouse, France.
June 2023, Applied Event Time Data Analysis with R (1-day course with Steven Chiou). 2023 ICSA Applied Statistics
Symposium, Ann Arbor, Michigan.
May 2022, Applied Event Time Data Analysis with R (1-day course with Steven Chiou). 2022 New England Statistics
Symposium, Storrs, CT.
July 2016, Advanced Statistical Computing (20-hour course). Shanghai University of Finance and Economics, Shanghai,
China.
June 2015, Advanced Statistical Computing (20-hour workshop). Shanghai University of Finance and Economics, Shang-
hai, China.
April 2015, Modern Multivariate Statistical Learning: Methods and Applications (1-day short course with Kun Chen). The
29th NESS at University of Connecticut, Storrs, CT.
May 2014, Advanced Statistical Computing (32-hour course). Renmin University of China, Beijing China.
April 2013, Statistical Analysis of Spatial Data and Visualization with Google Map (1-day short course with Marcos Prates).
The 27th NESS at University of Connecticut, Storrs, CT.
November 2012, Introduction to the Theory and Practice of Copulas (1-week short course). Universidade Federal de Minas
Gerais, Belo Horizonte, Minas Gerais, Brazil.
April 2011, Introduction to the Theory and Practice of Copulas (1-day short course with Ivan Kojadinovic). The 25th NESS
at University of Connecticut. Storrs, CT.
May 2008, Introduction to the Theory and Practice of Copulas (32-hour course). Renmin University of China, Beijing,
China.
October, 2024