Papers

Journal Articles

  • Kokoszka, P., Mohammadi, N., Santoro, L., Miao, H. and Wang, H. (2026+). Functional Vašiček model. Journal of Time Series Analysis, in press.
  • Kokoszka, P., Mohammadi, N., Wang, H. and Wang, S. (2025). Functional diffusion driven stochastic volatility model. Bernoulli, 31, 922-947.
  • Zhang, Z., Ding, P., Zhou, W. and Wang, H. (2025) With random regressors, least squares inference is robust to correlated errors with unknown correlation structure. Biometrika, 112, asae054.
  • Rimkus, M., Kokoszka, P. Duan, D., Wang, X. and Wang, H. (2025). Graph neural networks for the localization of faults in partially observed regional transmission systems. Scandinavian Journal of Statistics, 52, 572–594.
  • Kokoszka, P., Mohammadi, N. and Wang, H. (2025). Dependence properties of stochastic volatility models. Journal of Time Series Analysis, 46, 421-431.
  • Zhou, T., Graham, J.E., Davalos-DeLosh D., Maulik, A., Edelstein, J., Hoffman, A., Wang, H. and Davalos, D. (2025). Prediction tool for discharge disposition and 30-day readmission using electronic health records among patients hospitalized for traumatic brain injury. Frontiers in Neurology, 16, 1581176.
  • Sun, H., Duan, D., Zhang, H., Choi, S., Zhang, J., Wang, H., Luo, J. and Yang, L. (2025). Data Manipulation Attacks Detection for Power Systems via Nonlinear Spatial-Temporal Modeling. IEEE Open Access Journal of Power and Energy, 12, 366-377.
  • Wang, Y., Ludwig, G., Chu, T., Wang, H. and Zhu, J. (2025). Bayesian Analysis and Variable Selection for Spatial Count Data with an Application to Rio De Janeiro Gun Violence. Spatial Statistics, 67, 100890.
  • Kutta, T., Jach, A., Haddad, M.F.C., Kokoszka, P. and Wang, H. (2025). Detection and localization of changes in a panel of densities. Journal of Multivariate Analysis, 205, 105374.
  • Stanton, G., Ramirez, D., Santamaria, I., Scharf, L.L. and Wang, H. (2024). Multi-channel factor analysis: identifiability and asymptotics. IEEE Transactions on Signal Processing, 72, 3562-3577.
  • Kokoszka, P., Kutta, T., Mohammadi, N., Wang, H. and Wang, S. (2024). Detection of a structural break in intraday volatility pattern. Stochastic Processes and their Applications, 176, 104426.
  • Kokoszka, P., Lin, M., Wang, H. and Hayne, S. (2024). Statistical risk quantification of two-directional internet traffic flows. Statistics in Transition, 25, 1-22.
  • Muramudalige, S. R., Jayasumana, A.P. and Wang, H. (2023). A feature mapping technique for complex data object generation with likelihood and deep generative approaches. IEEE Access, 11, 136643-136653.
  • Kokoszka, P., Kutta, T., Singh, D. and Wang, H. (2023). Limit theorems for a higher order time dependent Markov chain model. Probability and Mathematics Statistics, 43, 121-139.
  • Rimkus, M., Kokoszka, P., Prabakar, K. and Wang, H. (2023). Toward real-time power grid fault detection. Communications in Statistics: Case Studies, Data Analysis and Applications, 9, 196-217.
  • Kokoszka, P., Rimkus, M., Hosur, S.S., Duan, D. and Wang, H. (2023). Detection and localization of faults in a regional power grid. Austrian Journal of Statistics, 52, 143-162.
  • Shirazi, H., Shashika, R.M., Ray, I., Jayasumana, A.P. and Wang, H. (2023). Adversarial autoencoder data synthesis for enhancing machine learning-based phishing detection algorithms. IEEE Transactions on Services Computing, 16, 2411-2422.
  • Chu, T., Liu, J., Zhu, J. and Wang, H. (2022). Spatio-temporal expanding distance asymptotic framework for locally stationary processes. Sankhya A, 84, 689-713.
  • Liu, J., Chu, T., Zhu, J. and Wang, H. (2022). Large spatial data modeling and analysis: a Krylov subspace approach. Scandinavian Journal of Statistics, 49, 1115-1143.
  • Kokoszka, P., Singh, D. and Wang, H. (2022). Long term behavior of incomplete and time varying product ratings. Statistics and Probability Letters, 184, 109387.
  • Zhang, L., Zhou, W. and Wang, H. (2022). Non-asymptotic properties of spectral decomposition of large Gram-type matrices and applications. Bernoulli, 28, 1224-1249.
  • Vaca, F.E., Li, K., Gao, X., Zagnoli, K., Wang, H., Haynie, D., Fell, J.C., Simons-Morton, B. and Romano, E. (2021). Time to licensure for driving among U.S. teens: survival analysis of interval-censored survey data. Traffic Injury Prevention, 22, 431-436.
  • Zhang, L., Zhou, W. and Wang, H. (2021). A semiparametric latent factor model for large scale temporal data with heteroscedasticity. Journal of Multivariate Analysis, 186, 104786.
  • Liu, J., Chu, T., Zhu, J. and Wang, H. (2021). Semiparametric method and theory for continuously indexed spatio-temporal processes. Journal of Multivariate Analysis, 183, 104735.
  • Kokoszka, P., Nguyen, H., Wang, H. and Yang, L. (2020). Statistical and probabilistic analysis of interarrival and waiting times of Internet2 anomalies. Statistical Methods & Applications, 29, 727-744.
  • Zheng, X., Duan D., Yang, L. and Wang, H. (2020). Decomposed iterative optimal power flow with automatic regionalization. Energies, 13, 4987.
  • Fang, L., Wang, H., Cheng, X., Yang, L. and Cui, S. (2020). Mobile privacy: scalable ensemble matching for user identification attacks. IEEE Access, 8, 97243-97257.
  • Tu, C.Y., Park, J. and Wang, H. (2020). Estimation of functional sparsity in nonparametric varying coefficient models for longitudinal data analysis. Statistica Sinica, 30, 439-465.
  • Chu, T., Zhu, J. and Wang, H. (2019). Semiparametric modeling with nonseparable and nonstationary spatio-temporal covariance functions and its inference. Statistica Sinica, 29, 1233-1252.
  • Fang, L., Cheng, X., Wang, H. and Yang, L. (2019). Idle time window prediction in cellular networks with deep spatiotemporal modeling. IEEE Journal on Selected Areas in Communications, 37, 1441-1454.
  • Fang, L., Cheng, X., Yang, L. and Wang, H. (2018). Location privacy in mobile big data: user identifiability via habitat region representation. Journal of Communications and Information Networks, 3, 31-38.
  • Wang, F. and Wang, H. (2018). Modeling non-stationary multivariate time series of counts via common factors. Journal of the Royal Statistical Society, Series B, 80, 769-791.
  • Fang, L., Cheng, X., Wang, H. and Yang, L. (2018). Mobile demand forecasting via deep graph-sequence spatiotemporal modeling in cellular networks. IEEE Internet of Things Journal, 5, 3091-3101.
  • Sienkiewicz, E. and Wang, H. (2018). Pareto quantiles of unlabeled tree objects. The Annals of Statistics, 46, 1513-1540.
  • Meyer, M.C., Kim, S. and Wang, H. (2018). Convergence rates for constrained regression splines. Journal of Statistical Planning and Inference, 193, 179-188.
  • Santamaria, I., Scharf, L.L., Via, J., Wang, H. and Wang, Y. (2017). Passive detection of correlated subspace signals in two MIMO channels. IEEE Transactions on Signal Processing, 65, 5266-5280.
  • Sienkiewicz, E., Song, D., Breidt, F.J. and Wang, H. (2017). Sparse functional dynamical models --- a big data approach. Journal of Computational and Graphical Statistics, 26, 319-329.
  • Lin, Z., Cao, J., Wang, L. and Wang, H. (2017). Locally Sparse estimator for functional linear regression models. Journal of Computational and Graphical Statistics, 26, 306-318.
  • Koehler, K., Zhu, J., Wang, H. and Peters, T. (2017). Sampling strategies for accurate hazard mapping of noise and other hazards using short-duration measurements. Annals of Work Exposures and Health, 61, 183-194.
  • Ludwig, G., Chu, T., Zhu, J., Wang, H. and Koehler, K. (2017). Static and roving sensor data fusion for spatio-temporal hazard mapping with application to occupational exposure assessment. The Annals of Applied Statistics, 11, 139-160.
  • Hayne, S., Wang, H. and Wang, L. (2015). Modeling reputation as a time-series: evaluating the risk of purchase decisions on eBay. Decision Sciences, 46, 1077-1107.
  • Wang, H. and Kai, B. (2015). Functional sparsity: global versus local. Statistica Sinica, 25, 1337-1354.
  • Lake, K., Zhu, J., Wang, H., Volckens, J. and Koehler, K. (2015). Effects of data sparsity and spatiotemporal variability on hazard maps of workplace noise. Journal of Occupational and Environmental Hygiene, 12, 256-265.
  • Wang, J., Opsomer, J.D. and Wang, H. (2014). Bagging nondifferentiable estimators in complex surveys. Survey Methodology, 40, 189-209.
  • Sienkiewicz, E. and Wang, H. (2014). Discussion: OODA of graph and tree structured data. Biometrical Journal, 56, 778-780.
  • Chu, T., Wang, H. and Zhu, J. (2014). On semiparametric inference of geostatistical models via local Karhunen-Loeve expansion. Journal of the Royal Statistical Society, Series B, 76, 817-832.
  • Wang, Y., Wang, H. and Scharf, L.L. (2014). Optimum compression of a noisy measurement for transmission over a noisy channel. IEEE Transactions on Signal Processing, 62, 1279-1289.
  • Wang, Y., Wang, H. and Scharf, L.L. (2014). The geometry of fusion inspired channel design. Signal Processing, 99, 136-146.
  • Song, D., Wang, H., Tu, C.Y., Marmarelis, V.Z., Hampson, R.E., Deadwyler, S.A. and Berger, T.W. (2013). Identification of sparse neural functional connectivity using penalized likelihood estimation and basis functions. Journal of Computational Neuroscience, 35, 335-357.
  • Chang, H.-W., Iyer, H., Bullitt, E. and Wang, H. (2013). Generalized linear mixed models for branching probabilities of brain artery systems. Model Assisted Statistics and Applications, 8, 121-133.
  • Wang, Y., Marron, J.S., Aydin, B., Ladha, A., Bullitt, E. and Wang, H. (2012). A nonparametric regression model with tree-structured response. Journal of the American Statistical Association, 107, 1272-1285.
  • Tu, C.Y., Song, D., Breidt, J.F., Berger, T.W. and Wang, H. (2012). Functional model selection for sparse binary time series with multiple inputs. Economic Time Series: Modeling and Seasonality, in honor of David Findley, 477-497.
  • Hayne, S., Wang, H. and Mendonca, S. (2012). eBay as the ``terminator": determining user suspension from feedback ratings. Journal of Organizational Computing and Electronic Commerce, 22, 160-183.
  • Aue, A., Lee, T.C.M. and Wang, H. (2012). Local bandwidth selection via second derivative segmentation. Electronic Journal of Statistics, 6, 478-500.
  • Aydin, B., Pataki, G., Wang, H., Ladha, A., Bullitt, E. and Marron, J.S. (2012). New approaches to principal component analysis for trees. Statistics in Biosciences, 4, 132-156.
  • Chu, T., Zhu, J. and Wang, H. (2011). Penalized maximum likelihood estimation and variable selection in geostatistics. The Annals of Statistics, 39, 2607-2625.
  • Al-Qawasmeh, A.M., Maciejewski, A.A., Wang, H., Smith, J., Siegel, H.J. and Potter, J. (2011). Statistical measures for quantifying task and machine heterogeneities. The Journal of Supercomputing, Special Issue on Advances in Parallel and Distributed Computing, 57, 34-50.
  • Aydin, B., Pataki, G., Wang, H., Ladha, A., Bullitt, E. and Marron, J.S. (2011). Visualizing the structure of large trees. Electronic Journal of Statistics, 5, 405-420.
  • Hayne, S.C., Bugbee, B. and Wang, H. (2010). Bidder behaviours on ebay: collectibles and commodities. Electronic Markets, 20, 95-104.
  • Aydin, B., Pataki, G., Wang, H., Bullitt, E. and Marron, J.S. (2009). A principal component analysis for trees. The Annals of Applied Statistics, 3, 1597-1615.
  • Wang, H., Cao, X. and Iyer, H. (2009). Estimation of the proportion of differentially expressed genes using hellinger distance. Statistics in Biosciences, 1, 246-267.
  • Wang, H. and Zhu, J. (2009). Variable selection in spatial regression via penalized least squares. The Canadian Journal of Statistics, 37, 607-624.
  • Sonderegger, D., Wang, H., Huang, Y. and William, C. (2009). Effects of measurement error on the strength of concentration-response relationships in aquatic toxicology. Ecotoxicology, 18, 824-828.
  • Sonderegger, D., Wang, H., William, C. and Noon, B. (2009). Using SiZer to detect thresholds in ecological data. Frontiers in Ecology and the Environment, 7, 190-195.
  • Wang, H. (2009). Measures of agreement for vectorcardiography data. Statistics in Medicine, 28, 1093-1107.
  • Wang, H. and Lee, T. C. M. (2008). Extraction of curvilinear features from noisy point patterns using principal curves. Pattern Recognition Letters, 29, 2078-2084.
  • Wang, H. and Marron, J. S. (2007). Object oriented data analysis: sets of trees. The Annals of Statistics, 35, 1849-1873.
  • Wang, H. and Iyer, H. (2007). Application of local linear embedding to nonlinear exploratory latent structure analysis. Psychometrika, 72, 199-225.
  • Wang, H. and Ranalli, M. G. (2007). Low-rank smoothing splines on complex domains. Biometrics, 63, 209-217.
  • Wang, H. and Lee, T. C. M. (2006). Automatic parameter selection for a $k$-segments algorithm for computing principal curves. Pattern Recognition Letters, 27, 1142-1150.

Refereed Proceedings

  • Stanton, G., Ramirez, D., Santamaria, I., Scharf, L.L. and Wang, H. (2023). Identifiability in multi-channel factor analysis. Asilomar Conference on Signals, Systems, and Computers, 1344-1349.
  • Stanton, G., Wang, H., Duan, D. and Louis L.L. (2023). Multi-channel factor analysis for temporally and spatially correlated time series. Asilomar Conference on Signals, Systems, and Computers, 1339-1343.
  • Shirazi, H., Pickard, W., Ray, I. and Wang, H. (2022). Towards resiliency of heavy vehicles through compromised sensor data reconstruction. Proceedings of the Twelfth ACM Conference on Data and Application Security and Privacy, 276-287.
  • Kattadige, C., Muramudalige, S.R., Choi, K.N., Jourjon, J., Wang, H., Jayasumana, A. and Thilakarathna, K. (2021). VideoTrain: a generative adversarial framework for synthetic video traffic generation. 2021 IEEE 22nd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM).
  • McAndrew, R., Hayne, S. and Wang, H. (2020). An unsupervised approach to ddos attack detection and mitigation in near-real time. Proceedings of the 53rd Hawaii International Conference on System Sciences, 6466-6475.
  • McAndrew, R., Hayne, S. and Wang, H. (2019). The benefits of a functional approach to detecting and mitigating a ddos attack. Fourteenth International Conference on Internet Monitoring and Protection (ICIMP).
  • McAndrew, R., Hayne, S. and Wang, H. (2019). Comparison of supervised and unsupervised learning for detecting anomalies in network traffic. Proceedings of the 52nd Hawaii International Conference on System Sciences, 7136-7145.
  • McAndrew, R., Gharaibeh, M., Wang, H., Hayne, S. and Papadopoulos, C. (2017). A functional approach to scanner detection. Proceedings of the Asian Internet Engineering Conference (AINTEC), 38-45.
  • Wang, Y., Scharf, L. L., Santamaria, I. and Wang, H. (2016). Canonical correlations for target detection in a passive radar network. Asilomar Conference on Signals, Systems, and Computers, 1159-1163.
  • Wang, Y., Wang, H. and Scharf, L.L. (2013). Scaled canonical coordinates for compression and transmission of noisy sensor measurements. Asilomar Conference on Signals, Systems, and Computers, 409-413.
  • Wang, Y., Wang, H. and Scharf, L.L. (2013). Fusion inspired channel design. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 5392-5396.
  • Song, D., Wang, H., and Berger, T.W. (2010). Estimating sparse volterra models using group $l_1$-regularization. Proceedings of the 32nd Annual International Conference of the IEEE EMBS, 4128-4131.
  • Wang, H. and Lee, T. C. M. (2007). Curvilinear feature extraction for noisy point pattern images. IEEE International Conference on Multimedia & Expo, 1635-1638.
  • Lee, T. C. M. and Wang, H. (2006). On a $k$-segments algorithm for computing principal curves. Seventh IEEE Southwest Symposium on Image Analysis and Interpretation, 183-187.

Others

  • Song, D., Wang, H., Marmarelis, V., Hampson, R., Deadwyler, S., Berger, T. (2013). Estimation of sparse dynamical model of neural functional connectivity using group lasso and laguerre basis functions. 2013 IEEE EMBC Short Papers, No. 3126.
  • Song, D., Wang, H., Hampson, R.E., Deadwyler, S.A., Berger, T.W. (2010). Identification of functional hippocampal CA3-CA1 connectivity using a group regularized likelihood method. Society for Neuroscience Abstracts, 99.28.
  • Wang, H. (2009). Review of "Nonlinear Dimensionality Reduction''. Biometrics, 65, 665.

Ackownledgement

My research has been supported by NSF grants DMS-0706761, DMS-0854903, DMS-1106975, DMS-1521746, DMS-1737795, DMS-1923142, CNS-1932413, and DMS-2123761 as well as grants with other collaborators.