Skip to main content

Research

Acknowledgement of grant support:

  • PI, NSF, DMS-2152289, 2022-2025. Website.
  • PI, NSF, DMS-1916237. 2019-2022.
  • PI, NSF, IIS-1633212. 2016-2019.
  • PI, NSF, DMS-1613112. 2016-2019.
  • PI, NSF, DMS–1309619. 2013-2015.

 

Issued Patent:

  • US Patent 10296555, “Methods, systems, and computer readable media for non-parametric dependence detection using bitwise operations in a computing system”, 2019. Inventors: Kai Zhang, Michael Thomas Max Baiocchi, Zhigen Zhao.

 

Preprints:

  • Brown, B., Zhang, K., and Meng, X-L. BELIEF in Dependence: Leveraging Atomic Linearity in Data Bits for Rethinking Generalized Linear Models, Paper.
  • Zhang, K., Zhao, Z., and Zhou, W. (2021). BEAUTY Powered BEAST. arXiv:2103.00674. Paper. R Package
  • Brown, B. and Zhang, K. The AUGUST Two-Sample Test: Powerful, Interpretable, and Fast. Paper.
  • Xiang, S., Zhang, W., Liu, S., Hoadley, K., Perou, C. M., Zhang, K., and Marron, J. S.. Pairwise Nonlinear Dependence Analysis of Genomic Data. arXiv:2202.09880.
  • Hoffman, K., Lees, J., and Zhang, K., Local Change Point Detection and Cleaning of EEMD Signals. arXiv:2103.01352.
  • Mosso, C., Bodwin, K., Chakraborty, S., Zhang, K., and Nobel, A., Latent Association Mining in Binary Data. arXiv: 1711.10427.

 

Publications:

  • Li, J., Zhang, W., Wang, P., Li, Q., Zhang, K., and Liu, Y. (2022). Nonparametric prediction distribution from resolution-wise regression with heterogeneous data. Journal of Business and Economic Statistics, to appear.
  • An, H., Zhang, K., Oja, H. and Marron, J. S. (2022). Variable Screening based on Gaussian Centered L-moments. Computational Statistics and Data Analysis, to appear.
  • Lee, D., Zhang, K., and Kosorok, M. R. (2022). The binary expansion randomized ensemble test. Statistica Sinica, to appear.
  • Lee, D., El-Zaatari, H., Kosorok, M., Li, X., and Zhang, K. (2022). Discussion of “Multiscale Fisher’s Independence Test for Multivariate Dependence”. Biometrika, 109, 593–596.
  • Gong, S., Zhang, K., and Liu, Y. (2021). Penalized linear regression with high-dimensional pairwise screening, Statistica Sinica, 31, 391-420.
  • Hoffman, K., Hannig, J., Zhang, K. (2021). Comments on: A Gibbs sampler for a class of random convex polytopes. Journal of the American Statistical Association, 116, 1206-1210.
  • Zhang, K. (2019). BET on Independence, Journal of the American Statistical Association, 114, 1620-1637. Paper.
  • Buja, A., Berk, R., Brown, L. D., George, E., Pitkin, E., Traskin, M., Zhao, L., and Zhang, K. (2017). Models as Approximations —A Conspiracy of Random Predictors and Model Violations against Classical Inference in Regression. Statistical Science, 34, no. 4, 523-544.. Paper.
  • Gong, S., Zhang, K., and Liu, Y. (2018). Efficient Testing-based Variable Selection for High-dimensional Linear Models, Journal of Multivariate Analysis, 166, 17-31.
  • Bodwin, K., Zhang, K., and Nobel, A. (2018). A Testing-based Approach to the Discovery of Differentially Correlated Variable Sets. Annals of Applied Statistics, 12(2), 1180-1203.
  • McCarthy, D., Zhang, K., Brown, L. D., Berk, R., Buja, A., George, E. and Zhao, L. (2018). Calibrated Percentile Double Bootstrap for Robust Linear Regression Inference. Statistica Sinica, 28, 2565-2589. Paper.
  • Zhang, K. (2017). Spherical Cap Packing Asymptotics and Rank-Extreme Detection. IEEE Transactions on Information Theory, 63(7), 4572-4584. Paper.
  • Yu, Q., Risk, B., Zhang, K., and Marron, J. S. (2017). JIVE Integration of Imaging and Behavioral Data. NeuroImage. 152, 38-49. Paper.
  • Lu, S., Liu, Y., Yin, L. and Zhang, K. (2017). Confidence Intervals and Regions for the LASSO Using Stochastic Variational Inequality Techniques in Optimization. Journal of the Royal Statistical Society, Series B, 79(2), 589-611. Paper.
  • Chen, D., Chen, X., and Zhang, K. (2016). An Exploratory Statistical Cusp Catastrophe Model. 2016 IEEE International Conference on Data Science and Advanced Analytics, Montreal, Canada, October. Paper.
  • Zhang. K. and Chen. D. (2016). Overcoming the Computing Barriers in Statistical Causal Inference. Statistical Causal Inferences and Their Applications in Public Health Research. 125-137, H. He et al. ed., ICSA Book Series in Statistics, Springer. Paper.
  • Brown, M., Koroluk, L. D., Ko, C., Zhang, K., Chen, M. and Nguyen, T. (2015). Effectiveness and Efficiency of a CAD/CAM-designed Orthodontic Bracket System. American Journal of Orthodontics & Dentofacial Orthopedics, 148, 6, 1067–1074. Paper.
  • Zhang, K., Brown, L. D., George, E. and Zhao, L. (2014). Uniform Correlation Mixture of Bivariate Normal Distributions and Hypercubically-contoured Densities That Are Marginally Normal. The American Statistician, 68(3), 183-187. Paper.
  • Berk, R., Brown, L. D., Buja, A., George, E., Pitkin, E., Zhang, K. and Zhao, L. (2014). Misspecified Mean Function Regression: Making Good Use of Regression Models That Are Wrong. Sociological Methods and Research, 43, 422-451. Paper.
  • Berk, R., Brown, L., George, E., Pitkin, E., Traskin, M., Zhang, K. and Zhao, L. (2013). What You Can Learn From Wrong Causal Models. Handbook of Causal Analysis for Social Research, 400-424, S. Morgan, ed., New York: Springer. Paper.
  • Berk, R., Brown, L. D., Buja, A., Zhang, K. and Zhao, L. (2013). Valid Post-Selection Inference. The Annals of Statistics, 41(2), 802-837. Paper.
  • Zhang, K., Traskin, M. and Small, D. (2012). A Powerful and Robust Test Statistic for Randomization Inference in Group-Randomized Trials with Matched Pairs of Groups. Biometrics, Volume 68, 75-84. Paper.
  • Zhang, K., Small, D., Lorch, S., Srinivas, S. and Rosenbaum, P. (2011). Using Split Samples and Evidence Factors in an Observational Study of Neonatal Outcomes. Journal of the American Statistical Association, Volume 106, 511-524. Paper.
  • Piette, J., Anand, S. and Zhang, K. (2010). Scoring and Shooting Abilities of NBA Players. Journal of Quantitative Analysis in Sports: Vol. 6 : Iss. 1 , Article 1. Paper.
  • Zhang, K. and Small, D. (2009). Comment: The Essential Role of Pair Matching in Cluster Randomized Experiments with Application to the Mexican Universal Health Insurance Evaluation. Statistical Science, Volume 24 , Number 1, 59-64. Paper.
  • Zhang, K. (2008). Limiting Distribution of Decoherent Quantum Random Walks. Physical Review A, 77, 062302. Paper.
  • Yang, W., Liu, C. and Zhang, K. (2007). A Path Integral Formula with Applications to Quantum Random Walks in Zd. Journal of Physics, Volume A 40-8487-8516. Paper.