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Wang, HaiYing   王海鹰

Department of Statistics
University of Connecticut

W319 Philip E. Austin Building
215 Glenbrook Rd. U-4120
Storrs, CT 06269-4120
Phone: (860) 486-6142

Email: \(\textrm{W}\)@\(\textrm{H}\).\(\textrm{Y}\),
  where \(\begin{cases} \textrm{W} &= \textrm{haiying.wang}\\ \textrm{H} &= \textrm{uconn}\\ \textrm{Y} &= \textrm{edu} \end{cases}\)

About Me

• I am a Professor, and was an Associate Professor, Assistant Professor in the Department of Statistics, at the University of Connecticut.
• I was an Assistant Professor in the Department of Mathematics & Statistics, at the University of New Hampshire.
• I obtained my PhD from the Department of Statistics, at the University of Missouri, with adviser Professor Nancy Flournoy.
• I obtained my MS from the Division of Statistical Science, Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, under the supervision of Professor Guohua Zou.
• I worked as an engineer in the R & D center of Midea Group.
• I obtained my BE from the School of Aeronautical Science and Engineering, Beijing University of Aeronautics and Astronautics.
• Here is my CV.

Research Interests

• Imbalanced and Rare Events data
• Incomplete data analysis
• Model selection and model averaging
• Nonparametric and semi-parametric regression
• Optimum experimental design
• Sub-sample methods for big data

Publications

1. Shan, J., Wang, L., & Wang, H. (2026). Optimal response-free cluster subsampling for longitudinal data under measurement constraints. Statistica Sinica. https://doi.org/10.5705/ss.202026.0022
2. Zhang, H., & Wang, H. (2026). Distributed subsampling strategy for partially linear additive models with massive data. Communications in Mathematics and Statistics, accepted.
3. Zhang, H., & Wang, H. (2026). Refitted cross-validation estimation for high-dimensional subsamples from low-dimension full data. Computational Statistics, 41(2), 38. https://doi.org/10.1007/s00180-025-01702-6
4. Shao, Y., Wang, L., Lian, H., & Wang, H. (2025). Optimal subsampling for high-dimensional partially linear models via machine learning methods. Journal of Machine Learning Research, 26(198), 1–70. http://jmlr.org/papers/v26/23-1475.html pdf
5. McGowan, L. D., Tass, S., Tyner, S., Wang, H., & Yan, J. (2025). Data jamboree: A party of open-source software solving real-world data science problems. The New England Journal of Statistics in Data Science, 1–9. https://doi.org/10.51387/25-NEJSDS79 pdf
6. Gao, J., Wang, L., & Wang, H. (2025). Power enhancing probability subsampling using side information. Statistics and Computing, 35(2), 28. https://doi.org/10.1007/s11222-024-10556-9 pdf
7. Wang, J., Wang, H., & Zhang, H. H. (2024). Scale-invariant optimal sampling for rare-events data and sparse models. Advances in Neural Information Processing Systems (NeurIPS), 37, 98384–98418. pdf
8. Zhang, H., Li, Y., & Wang, H. (2024). DsubCox: A fast subsampling algorithm for cox model with distributed and massive survival data. International Journal of Biostatistics, Accepted. https://doi.org/10.1515/ijb-2024-0042 pdf
9. Yu, J., Wang, H., & Ai, M. (2025). A subsampling strategy for AIC-based model averaging with generalized linear models. Technometrics, 67(1), 122–132. https://doi.org/10.1080/00401706.2024.2407310 pdf
10. Yang, Z., Wang, H., & Yan, J. (2024). Optimal subsampling for semi-parametric accelerated failure time models with massive survival data using a rank-based approach. Statistics in Medicine, 43(24), 4650–4666. https://doi.org/https://doi.org/10.1002/sim.10200 pdf
11. Zhang, Y., Wang, L., Zhang, X., & Wang, H. (2024). Independence-encouraging subsampling for nonparametric additive models. Journal of Computational and Graphical Statistics, 33(4), 1424–1433. https://doi.org/10.1080/10618600.2024.2326136 pdf
12. Yang, Z., Wang, H., & Yan, J. (2024). Subsampling approach for least squares fitting of semi-parametric accelerated failure time models to massive survival data. Statistics and Computing, 34(2), 1–11. https://rdcu.be/dyFzJ pdf
13. Wang, J., Wang, H., & Chen, K. (2023). Discussion of “Statistical inference for streamed longitudinal data”. Biometrika, 110(4), 863–866. https://doi.org/10.1093/biomet/asad035 pdf
14. Zhang, H., Zuo, L., Wang, H., & Sun, L. (2024). Approximating partial likelihood estimators via optimal subsampling. Journal of Computational and Graphical Statistics, 33(1), 276–288. https://doi.org/10.1080/10618600.2023.2216261 pdf
15. Yu, J., Liu, J., & Wang, H. (2023). Information-based optimal subdata selection for non-linear models. Statistical Papers, 64(4), 1069–1093. https://doi.org/10.1007/s00362-023-01430-3 pdf
16. Wang, Z., Wang, H., & Ravishanker, N. (2023). Subsampling in longitudinal models. Methodology and Computing in Applied Probability, 25(1), 35. https://doi.org/10.1007/s11009-023-10015-4 pdf
17. Kim, J. K., & Wang, H. (2023). A note on weight smoothing in survey sampling. Survey Methodology, 49(1), 31–38. pdf
18. Yao, Y., Zou, J., & Wang, H. (2023). Model constraints independent optimal subsampling probabilities for softmax regression. Journal of Statistical Planning and Inference, 225, 188–201. https://doi.org/10.1016/j.jspi.2022.12.004 pdf
19. Wang, H. (2022). A note on centering in subsample selection for linear regression. Stat, 11(1), e525. https://doi.org/10.1002/sta4.525 pdf
20. Wang, H., & Kim, J. K. (2022). Maximum sampled conditional likelihood for informative subsampling. Journal of Machine Learning Research, 23(332), 1–50. http://jmlr.org/papers/v23/21-0506.html pdf
21. Yang, Z., Wang, H., & Yan, J. (2022). Optimal subsampling for parametric accelerated failure time models with massive survival data. Statistics in Medicine, 41(27), 5421–5431. https://doi.org/10.1002/sim.9576 pdf
22. Zhu, R., Wang, H., Zhang, X., & Liang, H. (2023). A scalable frequentist model averaging method. Journal of Business & Economic Statistics, 41(4), 1228–1237. https://doi.org/10.1080/07350015.2022.2116442 pdf
23. Wang, J., Wang, H., & Xiong, S. (2024). Unweighted estimation based on optimal sample under measurement constraints. Canadian Journal of Statistics, 52(1), 291–309. https://doi.org/https://doi.org/10.1002/cjs.11753 pdf
24. Lee, J., Schifano, E., & Wang, H. (2023). Sampling-based gaussian mixture regression for big data. Journal of Data Science, 21(1), 158–172. https://doi.org/10.6339/22-JDS1057 pdf
25. Wang, F., Wang, H., & Yan, J. (2023). Diagnostic tests for the necessity of weight in regression with survey data. International Statistical Review, 91(1), 55–71. https://doi.org/10.1111/insr.12509 pdf
26. Wang, J., Zou, J., & Wang, H. (2022). Sampling with replacement vs poisson sampling: A comparative study in optimal subsampling. IEEE Transactions on Information Theory, 68(10), 6605–6630. https://doi.org/10.1109/TIT.2022.3176955 pdf code
27. Yu, J., & Wang, H. (2022). Subdata selection algorithm for linear model discrimination. Statistical Papers, 63(6), 1883–1906. https://doi.org/10.1007/s00362-022-01299-8 pdf
28. Wang, H., Zhang, A., & Wang, C. (2021). Nonuniform negative sampling and log odds correction with rare events data. In M. Ranzato, A. Beygelzimer, Y. Dauphin, P. S. Liang, & J. W. Vaughan (Eds.), Advances in neural information processing systems (Vol. 34, pp. 19847–19859). Curran Associates, Inc. https://proceedings.neurips.cc/paper_files/paper/2021/file/a51c896c9cb81ecb5a199d51ac9fc3c5-Paper.pdf pdf code
29. Yao, Y., Zou, J., & Wang, H. (2023). Optimal poisson subsampling for softmax regression. Journal of Systems Science and Complexity, 36(4), 1609–1625. pdf
30. Wang, H., Zhang, D., Liang, H., & Ruppert, D. (2021). Iterative likelihood: A unified inference tool. Journal of Computational and Graphical Statistics, 30(4), 920–933. https://doi.org/10.1080/10618600.2021.1904961 pdf
31. Lee, J., Schifano, E., & Wang, H. (2024). Fast optimal subsampling probability approximation for generalized linear models. Econometrics and Statistics, 29, 224–237. https://doi.org/https://doi.org/10.1016/j.ecosta.2021.02.007 pdf
32. Wang, H., & Zou, J. (2021). A comparative study on sampling with replacement vs poisson sampling in optimal subsampling. In A. Banerjee & K. Fukumizu (Eds.), Proceedings of the 24th international conference on artificial intelligence and statistics (Vol. 130, pp. 289–297). PMLR. http://proceedings.mlr.press/v130/wang21a.html pdf
33. Zuo, L., Zhang, H., Wang, H., & Sun, L. (2021). Optimal subsample selection for massive logistic regression with distributed data. Computational Statistics, 36(4), 2535–2562. https://doi.org/10.1007/s00180-021-01089-0 pdf
34. Bar, H., & Wang, H. (2021). Reproducible science with LaTeX. Journal of Data Science., 19(1), 111–125. pdf code
35. Yao, Y., & Wang, H. (2021). A review on optimal subsampling methods for massive datasets. Journal of Data Science, 19(1), 151–172. pdf
36. Zuo, L., Zhang, H., Wang, H., & Liu, L. (2021). Sampling-based estimation for massive survival data with additive hazards model. Statistics in Medicine, 40(2), 441–450. pdf
37. Zhang, H., & Wang, H. (2021). Distributed subdata selection for big data via sampling-based approach. Computational Statistics & Data Analysis, 153, 107072. https://doi.org/10.1016/j.csda.2020.107072 pdf
38. Pronzato, L., & Wang, H. (2021). Sequential online subsampling for thinning experimental designs. Journal of Statistical Planning and Inference, 212, 169–193. https://doi.org/10.1016/j.jspi.2020.08.001 pdf
39. Yao, Y., & Wang, H. (2021). A selective review on statistical techniques for big data. In Y. Zhao & (Din). D.-G. Chen (Eds.), Modern statistical methods for health research (pp. 223–245). Springer International Publishing. https://doi.org/10.1007/978-3-030-72437-5_11 pdf
40. Wang, H. (2020). Logistic regression for massive data with rare events. Proceedings of the 37th International Conference on Machine Learning (ICML), 119, 9829–9836. http://proceedings.mlr.press/v119/wang20a.html pdf code
41. Yu, J., Wang, H., Ai, M., & Zhang, H. (2022). Optimal distributed subsampling for maximum quasi-likelihood estimators with massive data. Journal of the American Statistical Association, 117(537), 265–276. https://doi.org/10.1080/01621459.2020.1773832 pdf
42. Cheng, Q., Wang, H., & Yang, M. (2020). Information-based optimal subdata selection for big data logistic regression. Journal of Statistical Planning and Inference, 209, 112–122. https://doi.org/10.1016/j.jspi.2020.03.004 pdf
43. Lee, J., Wang, H., & Schifano, E. D. (2020). Online updating method to correct for measurement error in big data streams. Computational Statistics & Data Analysis, 149, 106976. pdf
44. Hu, G., & Wang, H. (2021). Most likely optimal subsampled markov chain monte carlo. Journal of Systems Science and Complexity, 34(3), 1121–1134. pdf
45. Wang, H., & Ma, Y. (2021). Optimal subsampling for quantile regression in big data. Biometrika, 108(1), 99–112. https://doi.org/10.1093/biomet/asaa043 pdf code
46. Wang, H. (2019). More efficient estimation for logistic regression with optimal subsamples. Journal of Machine Learning Research, 20(132), 1–59. pdf code
47. Xue, Y., Wang, H., Yan, J., & Schifano, E. D. (2020). An online updating approach for testing the proportional hazards assumption with streams of survival data. Biometrics, 76(1), 171–182. https://doi.org/10.1111/biom.13137 pdf
48. Wang, H. (2019). Divide-and-conquer information-based optimal subdata selection algorithm. Journal of Statistical Theory and Practice, 13(3), 46. https://doi.org/10.1007/s42519-019-0048-5 pdf code
49. Ai, M., Yu, J., Zhang, H., & Wang, H. (2021). Optimal subsampling algorithms for big data regressions. Statistica Sinica, 31(2), 749–772. https://doi.org/10.5705/ss.202018.0439 pdf
50. Yao, Y., & Wang, H. (2019). Optimal subsampling for softmax regression. Statistical Papers, 60(2), 235–249. pdf
51. Wang, H., Yang, M., & Stufken, J. (2019). Information-based optimal subdata selection for big data linear regression. Journal of the American Statistical Association, 114(525), 393–405. pdf R Package R code
52. Wang, H., Zhu, R., & Ma, P. (2018). Optimal subsampling for large sample logistic regression. Journal of the American Statistical Association, 113(522), 829–844. pdf R Package R code
53. Zhang, X., Wang, H., Ma, Y., & Carroll, R. J. (2017). Linear model selection when covariates contain errors. Journal of the American Statistical Association, 112(520), 1553–1561. pdf Supplementary
54. Li, Y., He, X., Wang, H., & Sun, J. (2016). Joint analysis of longitudinal data and informative observation times with time-dependent random effects. In New developments in statistical modeling, inference and application (pp. 37–51). Springer. pdf
55. Lane, A., Wang, H., & Flournoy, N. (2016). Conditional inference in two-stage adaptive experiments via the bootstrap. In mODa 11-advances in model-oriented design and analysis (pp. 173–181). Springer. pdf
56. Li, Y., He, X., Wang, H., & Sun, J. (2016). Regression analysis of longitudinal data with correlated censoring and observation times. Lifetime Data Analysis, 22(3), 343–362. pdf
57. Wang, H., Chen, X., & Flournoy, N. (2016). The focused information criterion for varying-coefficient partially linear measurement error models. Statistical Papers, 57(1), 99–113. pdf
58. Li, Y., He, X., Wang, H., Zhang, B., & Sun, J. (2015). Semiparametric regression of multivariate panel count data with informative observation times. Journal of Multivariate Analysis, 140, 209–219. pdf
59. Wang, H., Schaeben, H., & Keidel, F. (2015). Optimized subsampling for logistic regression with imbalanced large datasets. Proceeding of the 17th Annual Conference of the International Association for Mathematical Geosciences, 1113–1119.
60. Wang, H., & Flournoy, N. (2015). On the consistency of the maximum likelihood estimator for the three parameter lognormal distribution. Statistics & Probability Letters, 105, 57–64. pdf
61. Wang, H., Li, Y., & Sun, J. (2015). Focused and model average estimation for regression analysis of panel count data. Scandinavian Journal of Statistics, 42(3), 732–745. pdf
62. Wang, H., Flournoy, N., & Kpamegan, E. (2014). A new bounded log-linear regression model. Metrika, 77(5), 695–720. pdf
63. Wang, H., & Zhou, S. Z. (2013). Interval estimation by frequentist model averaging. Communications in Statistics-Theory and Methods, 42(23), 4342–4356. pdf
64. Wang, H., Zou, G., & Wan, A. T. (2013). Adaptive LASSO for varying-coefficient partially linear measurement error models. Journal of Statistical Planning and Inference, 143(1), 40–54. pdf
65. Wang, H., Pepelyshev, A., & Flournoy, N. (2013). Optimal design for the bounded log-linear regression model. In mODa 10–advances in model-oriented design and analysis (pp. 237–245). Springer. pdf
66. Wang, H., Zou, G., Wan, A. T., et al. (2012). Model averaging for varying-coefficient partially linear measurement error models. Electronic Journal of Statistics, 6, 1017–1039. pdf
67. Wang, H., & Sun, D. (2012). Objective bayesian analysis for a truncated model. Statistics & Probability Letters, 82(12), 2125–2135. pdf
68. Wang, H., & Zou, G. (2012). Frequentist model averaging estimation for linear errors-in-variables models (in chinese). Journal of Systems Science and Mathematical Science, 32(2), 1–14. pdf
69. Kozak, M., & Wang, H. (2010). On stochastic optimization in sample allocation among strata. Metron, 68(1), 95–103. pdf
70. Wang, H., Zhang, X., & Zou, G. (2009). Frequentist model averaging estimation: A review. Journal of Systems Science and Complexity, 22(4), 732–748. pdf

Collaborative and Other Publications

1. Asadifakhr, J. A. P., Koorosh AND Huang. (2026). Bridging priorities: Stakeholder preferences, networks, and barriers in road-stream crossing management. PLOS ONE, 21(1), 1–16. https://doi.org/10.1371/journal.pone.0339740
2. Wang, H., Deng, X., Lin, D., Chen, M.-H., Xie, M., & Wu, J. (2023). Editorial. Design and analysis of experiments for data science. The New England Journal of Statistics in Data Science, 1(3), 297–298. https://doi.org/10.51387/23-NEJSDS13EDI pdf
3. Dey, D. K., Chen, M.-H., Xie, M., Wang, H., & Wu, J. (2023). Editorial. Modern bayesian methods with applications in data science. The New England Journal of Statistics in Data Science, 1(2), 123–125. https://doi.org/10.51387/23-NEJSDS12EDI pdf
4. Wu, C. O., Chen, M.-H., Xie, M., Wang, H., & Wu, J. (2023). Inaugural editorial. Can we achieve our mission: Fast, accessible, cutting-edge, and top-quality? The New England Journal of Statistics in Data Science, 1(1), 1–3. https://doi.org/10.51387/23-NEJSDS11EDI pdf
5. Jakositz, S., Ghasemi, R., McGreavy, B., Wang, H., Greenwood, S., & Mo, W. (2022). Tap water lead monitoring through citizen science: The influence of socioeconomics and participation on environmental literacy, behavior, and communication. Journal of Environmental Engineering, 148(10), 04022060. https://doi.org/10.1061/(ASCE)EE.1943-7870.0002055
6. Zhou, Y., Qiu, L., Wang, H., & Chen, X. (2020). Induction of activity synchronization among primed hippocampal neurons out of random dynamics is key for trace memory formation and retrieval. The FASEB Journal, 34(3), 3658–3676. https://doi.org/10.1096/fj.201902274R
7. Zhou, Y., Qiu, L., Sterpka, A., Wang, H., Chu, F., & Chen, X. (2019). Comparative phosphoproteomic profiling of type III adenylyl cyclase knockout and control, male, and female mice. Frontiers in Cellular Neuroscience, 13, 34.
8. Stang, S., Wang, H., Gardner, K. H., & Mo, W. (2018). Influences of water quality and climate on the water-energy nexus: A spatial comparison of two water systems. Journal of Environmental Management, 218, 613–621.
9. Mo, W., Wang, H., & Jacobs, J. M. (2016). Understanding the influence of climate change on the embodied energy of water supply. Water Research, 95, 220–229.
10. Feng, S., Ding, W., Wang, H., Yu, Z., Chen, Y., Zhang, Y., & Xiao, H. (2008). Sampling procedures for inspection by attributes-part 3: Skip-lot sampling procedures (in chinese). Chinese National Standard, GB/T2828.3-2008.

Teaching

• At the University of Missouri

  • Statistics 1200 - Introductory Statistical Reasoning (3cr.), Fall 2010, Spring 2011, Fall 2011
  • Statistics 2500 - Introductory to probability and statistics I (3cr.), Spring 2012
  • Statistics 3500 - Introductory to probability and statistics II (3cr.), Fall 2012, Spring 2013

• At the University of New Hampshire

  • Math 539 - Introduction to Statistical Analysis (4cr.), Fall 2014
  • Math 644 - Statistics for Engineers and Scientists (4cr.), Fall 2013, Spring 2014, Fall 2014
  • Math 736/836 - Advanced Statistical Methods for Research (4cr.), Spring 2014, Spring 2015, Spring 2016
  • Math 739/839 - Applied Regression Analysis (4cr.), Fall 2016
  • Math 755/855 - Probability with Applications (4cr.), Fall 2015, Fall 2016
  • Math 756/856 - Principles of Statistical Inference (4cr.), Spring 2016, Spring 2017
  • Math 969 - Topics in Probability and Statistics (3cr.), Spring 2017

• At the University of Connecticut

  • STAT 3115Q - Analysis of Experiments (3cr.), Spring, 2018
  • STAT 5125 - Computing for Statistical Data Science (3cr. in julia), Fall 2021, Spring 2022
  • BIST/STAT 5535: Nonparametric Methods (3cr. using julia) Fall 2018, 2020, 2021
  • BIST/STAT 5505 - Applied Statistics I (3cr.), Fall 2017, 2018, 2019
  • BIST/STAT 5605 - Applied Statistics II (3cr.), Spring 2019, 2020
  • BIST/STAT 6494: Statistical Inference for Big Data (3cr.) Spring 2018

Professional Service

• Associate Editor, 2023 - present, Statistica Sinica
• Associate Editor, 2022 - present, Sankhya B
• Associate Editor, 2022 - present, Statistics and Its Interface
• Managing Editor, 2021 - present, New England Journal of Statistics in Data Science

Membership

• American Statistical Association
• Institute of Mathematical Statistics
• International Chinese Statistical Association
• International Statistical Institute
• New England Statistical Society