Jing Lei

Working papers and preprints

1. Jing Lei, "Adaptive Global Testing for Functional Linear Models".
2. Vincent Q. Vu and Jing Lei, "Squared-norm Empirical Process in Banach Space".
3. Jing Lei, Alessandro Rinaldo, and Larry Wasserman, "A Conformal Prediction Approach to Explore Functional Data". [arXiv]
4. Vincent Q. Vu and Jing Lei (2012) "Minimax Sparse Principal Subspace Estimation in High Dimensions", [arXiv]

Peer-reviewed publications

1. Jing Lei and Larry Wasserman (2013+) "Distribution Free Prediction Bands for Nonparametric Regression", Journal of the Royal Statistical Society, Series B, to appear. [arXiv]
2. Jing Lei, James Robins, and Larry Wasserman (2013) "Distribution Free Prediction Sets", Journal of the American Statistical Association, 108, 278-287. [pdf]
3. Jing Lei and Peter Bickel (2013) "On convergence of recursive Monte Carlo filters in non-compact state spaces", Statistica Sinica, 23, 429-450. [pdf]
4. Vincent Vu and Jing Lei (2012) "Minimax rates of estimation for sparse PCA in high dimensions", in the Fifteenth International Conference on Artificial Intelligence and Statistics (AISTATS'12, Best Paper Award). [arXiv]
5. Jing Lei and Peter Bickel (2011) "A moment-matching approach to nonlinear non-Gaussian ensemble filtering", Monthly Weather Review, 139, 3964-3973. [pdf]
6. Jing Lei (2011) "Differentially private M-estimators", in Proceedings of the 25th Annual Conference on Neural Information Proceeding Systems (NIPS'11). [pdf], [supplementary]
7. Jing Lei and Peter Bickel (2010) "Comparison of ensemble Kalman filters under non-Gaussianity", Monthly Weather Review, 138, 1293-1306. [pdf]
8. Cynthia Dwork and Jing Lei (2009) "Differential privacy and robust statistics", in Proceedings of the 41st Annual ACM Symposium on Theory of Computing (STOC'09). [Extended Abstract] [Full Version]

Presentations

1. Distribution free prediction sets.
        Contributed talk, 14th Meeting of New Researchers in Statistics and Probability, University of California, San Diego, CA. July 2012.
2. Debiasing the ensemble Kalman filter: the NLEAF algorithm
        Invited talk, The National Center for Atmospheric Research (NCAR), Boulder, CO. February 2010.
3. Predicting the Chaos Using Ensemble Filters: A Regression Approach
        Poster, Theory and Practice of Computational Learning Summer Workshop, University of Chicago, IL. June 2009.
4. On Stability and Sparsity of ensemble Kalman filters
        Poster, Future Directions in High-Dimensional Data Analysis, Isaac Newton Institute, Cambridge, UK. June 2008.
5. Particle filters and their potential use in numerical weather forecasting
        Invited talk, The National Center for Atmospheric Research (NCAR), Boulder, CO. December 2007.