Publications


Pre-prints

  1. Jocelyn T. Chi and Chi, E. C. (2020). A User-Friendly Computational Framework for Robust Structured Regression Using the L_2 Criterion. arXiv:2010.04133 [stat.CO].
  2. Jocelyn T. Chi and Ipsen, I. C. F. (2020). Multiplicative Perturbation Bounds for Multivariate Multiple Linear Regression in Schatten p-Norms. arXiv:2007.06099 [math.NA].
  3. Jocelyn T. Chi and Ipsen, I. C. F. (2020). A Projector-Based Approach to Quantifying Total and Excess Uncertainties for Sketched Linear Regression. arXiv:1808.05924 [stat.ML].


Peer-Reviewed Publications

  1. Qin, J., Lee, H., Jocelyn T. Chi, Drumetz, L., Chanussot, J., Lou, Y. and Bertozzi, A. L. (2020). Blind Hyperspectral Unmixing Based on Graph Total Variation Regularization. IEEE Transactions on Geoscience and Remote Sensing, In press.
  2. Qin, J., Lee, H., Jocelyn T. Chi, Chanussot, J., Lou, Y. and Bertozzi, A. L. (2019). Fast Blind Hyperspectral Unmixing based on Graph Laplacian. 2019 IEEE Workshop on Hyperspectral Imaging and Signal Processing (WHISPERS).
  3. Jocelyn T. Chi, Chi, E. C. and Baraniuk, R. G. (2016). k-POD: A Method for k-Means Clustering of Missing Data. The American Statistician, 70(1), 91–99. doi:10.1080/00031305.2015.1086685
  4. Jocelyn T. Chi and Handcock, M. S. (2014). Identifying Sources of Healthcare Underutilization Among California’s Immigrants. Journal of Racial and Ethnic Health Disparities, 1(3), 207–218. doi:10.1007/s40615-014-0028-0


Unrefereed Articles

  1. Jocelyn T. Chi and Chi, E. C. (2014, March). Getting to the Bottom of Matrix Completion and Nonnegative Least Squares with the MM Algorithm. StatisticsViews.com.
  2. Jocelyn T. Chi and Chi, E. C. (2014, January). Getting to the Bottom of Regression with Gradient Descent. StatisticsViews.com.