HaiYing Wang    王海鹰

Department of Statistics
University of Connecticut

Room 319 Philip E. Austin Building
215 Glenbrook Rd. U-4120
Storrs, CT 06269-4120
Phone: (860) 486-6142

About Me

Research Interests

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


  1. Feng, S., Ding, W., Wang, H., Yu, Z., Chen, Y., Zhang, Y. and Xiao, H. (2008). Sampling procedures for inspection by attributes-Part 3: Skip-lot sampling procedures. In Chinese National Standard, GB/T2828.3-2008.
  2. Wang, H., Zhang, X. and Zou, G. (2009). Frequentist model averaging estimation: a review. Journal of Systems Science and Complexity, 22 (4), 732-748. pdf
  3. Kozak, M., Wang, H. (2010). On stochastic optimization in sample allocation among strata. METRON, LXVIII n.1, pp. 95-103. pdf
  4. Wang, H. and Zou, G. (2012). Frequentist Model Averaging Estimation for Linear Errors-in-Variables Models. Journal of Systems Science and Mathematical Science, 32 (2), 1-14. pdf
  5. Wang, H., Zou, G. and Wan, A. T. K. (2012). Model Averaging for Varying-Coefficient Partially Linear Measurement Error Models. Electronic Journal of Statistics, 6, 1017-1039.
  6. Wang, H. and Sun, D. (2012). Objective Bayesian analysis of a truncated model. Statistics and Probability Letters, 82, 2125-2135. pdf
  7. Wang, H., Zou, G. and Wan, A. T. K. (2013). Adaptive Lasso for Varying-Coefficient Partially Linear Measurement Error Models. Journal of Statistical Planning and Inference, 143, 40-54. pdf
  8. Wang, H. and Zhou, S. Z. F. (2013). Interval Estimation by Frequentist Model Averaging. Communications in Statistics - Theory and Method, 42, 4342-4356. pdf
  9. Wang, H., Pepelyshev, A. and Flournoy, N. (2013). Optimal design for a new bounded log-linear regression model. MoDa 10 - Advances in Model-Oriented Design and Analysis, 237-245. Springer. pdf
  10. Wang, H., Flournoy, N. and Kpamegan, E. (2014). A New Bounded Log-linear Regression Model. Metrika, 77, 695-720. pdf
  11. Wang, H., Chen, X. and Flournoy, N. (2016). The Focus Information Criterion and Model Averaging for Varying-Coefficient Partially Linear Measurement Error Models. Statistical Papers, 57, 99-113. pdf
  12. Wang, H., Li, Y. and Sun, J. (2015). Focused and Model Average Estimation for Panel Count Data. Scandinavian Journal of Statistics, 42, 732-745. pdf
  13. Wang, H. and Flournoy, N. (2015) On the consistency of the maximum likelihood estimation for the three parameter lognormal distribution. Statistics and Probability Letters, 105, 57-64. pdf
  14. Li, Y., He, X., Wang, H. and Sun, J. (2016) Regression Analysis of Longitudinal Data with Correlated Censoring and Observation Times. Lifetime Data Analysis, 22, 343-362. pdf
  15. Li, Y., He, X., Wang, H. and Sun, J. (2015) Semiparametric Regression of Multivariate Panel Count Data with Informative Observation Times. Journal of Multivariate Analysis, 140, 209-219. pdf
  16. Wang, H., Schaeben, H. and 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.
  17. Mo, W, Wang, H. and Jacobsa, J. (2016). Understanding the influence of climate change on the embodied energy of water supply. Water Research, 220-229.
  18. Lane, A., Wang, H. and Flournoy, N. (2016) Conditional inference in two-stage response-adaptive experiments via the bootstrap MoDa 11 - Advances in Model-Oriented Design and Analysis, 173-181. Springer. pdf
  19. Li, Y., He, X., Wang, H. and Sun, J. (2016) Joint Analysis of Longitudinal Data and Informative Observation Times with Time-Dependent Random Effects. New Developments in Statistical Modeling, Inference and Application, 37-51.
  20. Zhang, X., Wang, H., Ma, Y. and Carroll, R. J. (2017) Linear Model Selection when Covariates Contain Errors. Journal of the American Statistical Association, 1553-1561. pdf Supplementary
  21. Stang, S., Wang, H., Gardnera, K., 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
  22. Wang, H., Zhu, R. and Ma, P. (2018) Optimal Subsampling for Large Sample Logistic Regression. Journal of the American Statistical Association, 829-844. pdf R package
  23. Wang, H., Yang, M. and Stufken, J. (2019) Information-Based Optimal Subdata Selection for Big Data Linear RegressionJournal of the American Statistical Association, 393-405. pdf R Code    R Package
  24. Yao, Y. and Wang, H. (2019). Optimal subsampling for softmax regression. Statistical Papers, 60(2):235-249. pdf
  25. Zhou, Y., Qiu, L., Sterpka, A., Wang, H., Chu, F., and Chen, X. (2019). Comparative Phospho- proteomic Profiling of Type III Adenylyl Cyclase Knockout and Control, Male, and Female Mice. Frontiers in Cellular Neuroscience, 13, 34. https://doi.org/10.3389/fncel.2019.00034.
  26. Wang, H. (2019). Divide-and-Conquer Information-Based Optimal Subdata Selection Algorithm. Journal of Statistical Theory and Practice. DOI: 10.1007/s42519-019-0048-5. pdf Julia Code
  27. Ai, M., Yu, J., Zhang, H. and Wang, H. (2019). Optimal Subsampling Algorithms for Big Data Generalized Linear Models. Statistica Sinica. DOI: 10.5705/ss.202018.0439. pdf
  28. Wang, H. (2019). More Efficient Estimation for Logistic Regression with Optimal Subsample. Journal of Machine Learning Research, 20(132):1−59. pdf
  29. Xue, Y., Wang, H. , Yan, J., Schifano, E. (2019). An Online Updating Approach for Testing the Proportional Hazards Assumption with Streams of Survival Data. Biometrics. DOI:10.1111/biom.13137   pdf
  30. Zhou, Y., Qiu, L., Wang, H., and 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, DOI:10.1096/fj.201902274R
  31. Wang, H. and Ma, Y. (2020). Optimal subsampling for quantile regression in big data. Biometrika. Accepted. pdf


  • At the University of Missouri
    • Statistics 1200 - Introductory Statistical Reasoning, Fall 2010, Spring 2011, Fall 2011 (3cr.)
    • Statistics 2500 - Introductory to probability and statistics I, Spring 2012 (3cr.)
    • Statistics 3500 - Introductory to probability and statistics II, Fall 2012, Spring 2013 (3cr.)
  • At the University of New Hampshire
    • Math 539 - Introduction to Statistical Analysis, Fall 2014 (4cr.)
    • Math 644 - Statistics for Engineers and Scientists, Fall 2013, Spring 2014, Fall 2014 (4cr.)
    • Math 736/836 - Advanced Statistical Methods for Research, Spring 2014, Spring 2015, Spring 2016 (4cr.)
    • Math 739/839 - Applied Regression Analysis, Fall 2016 (4cr.)
    • Math 755/855 - Probability with Applications, Fall 2015, Fall 2016 (4cr.)
    • Math 756/856 - Principles of Statistical Inference, Spring 2016, Spring 2017 (4cr.)
    • Math 969 - Topics in Probability and Statistics, Spring 2017 (3cr.)
  • At the University of Connecticut
    • BIST/STAT 5505 - Applied Statistics I, Fall 2017, 2018, 2019 (3cr.)
    • STAT 3115Q - Analysis of Experiments (3cr.), Spring, 2018
    • BIST/STAT 6494: Statistical Inference for Big Data (3cr.) Spring, 2018
    • BIST/STAT 5535: Nonparametric Methods (3cr. taught using julia) Fall, 2018
    • BIST/STAT 5605 - Applied Statistics II, Spring 2019, 2020 (3cr.)