| A comparison of the lasso and marginal regression CR Genovese, J Jin, L Wasserman, Z Yao The Journal of Machine Learning Research 13 (1), 2107-2143, 2012 | 118 | 2012 |
| Optimal classification in sparse Gaussian graphic model Y Fan, J Jin, Z Yao The Annals of Statistics, 2537-2571, 2013 | 51 | 2013 |
| Principal flows VM Panaretos, T Pham, Z Yao Journal of the American Statistical Association 109 (505), 424-436, 2014 | 25 | 2014 |
| Partial correlation screening for estimating large precision matrices, with applications to classification S Huang, J Jin, Z Yao | 13 | 2016 |
| Principal Boundary on Riemannian Manifolds Z Yao, Z Zhang Journal of the American Statistical Association, 2019 | 8 | 2019 |
| Estimating the Number of Sources in Magnetoencephalography Using Spiked Population Eigenvalues Z Yao, Y Zhang, Z Bai, WF Eddy Journal Journal of the American Statistical Association, 2017 | 6 | 2017 |
| Fixed Boundary Flows Z Yao, Y Xia, Z Fan | 5* | 2019 |
| A Level Set Based Variational Principal Flow Method for Nonparametric Dimension Reduction on Riemannian Manifolds H Liu, Z Yao, S Leung, TF Chan SIAM J. Sci. Comput 39 (4), A1616–A1646, 2017 | 5 | 2017 |
| Manifold Fitting under Unbounded Noise Z Yao, Y Xia | 3 | 2019 |
| Estimating the Rate Constant from Biosensor Data via an Adaptive Variational Bayesian Approach Y Zhang, Z Yao, P Forssen, F Torgny Annals of Applied Statistics, 2019 | 3 | 2019 |
| Phase Transitions for High-Dimensional Quadratic Discriminant Analysis with Rare and Weak Signals W Wang, J Wu, Z Yao arXiv preprint arXiv:2108.10802, 2021 | 2 | 2021 |
| Quantifying Time-Varying Sources in Magnetoencephalography – A Discrete Approach Z Yao, Z Fan, M Hayashi, WF Eddy Annals of Applied Statistics, 2019 | 2 | 2019 |
| Supplementary material for “optimal classification in sparse Gaussian graphic model” Y Fan, J Jin, Z Yao | 1 | 2013 |
Jiashun JinProfessor of Statistics, Carnegie Mellon UniversityVerified email at stat.cmu.edu
