| Asymptotic normality of locally modelled regression estimator for functional data Z Zhiyong, L Zhengyan Journal of Nonparametric Statistics 28, 116-131, 2016 | 34 | 2016 |
| A new nonconvex sparse recovery method for compressive sensing Z Zhou, J Yu Frontiers in applied mathematics and statistics 5, 14, 2019 | 26 | 2019 |
| Sparse recovery based on q-ratio constrained minimal singular values Z Zhou, J Yu Signal Processing 155, 247-258, 2019 | 23 | 2019 |
| Adaptive algorithm for sparse signal recovery FL Bayisa, Z Zhou, O Cronie, J Yu Digital Signal Processing 87, 10-18, 2019 | 12 | 2019 |
| On q-ratio CMSV for sparse recovery Z Zhou, J Yu Signal Processing 165, 128-132, 2019 | 11 | 2019 |
| RIP analysis for the weighted ℓr-ℓ1 minimization method Z Zhou Signal Processing 202, 108754, 2023 | 10* | 2023 |
| Estimation of block sparsity in compressive sensing Z Zhou, J Yu International Journal of Wavelets, Multiresolution and Information …, 2022 | 9* | 2022 |
| Recovery analysis for weighted mixed ℓ2∕ ℓp minimization with 0< p≤ 1 Z Zhou, J Yu Journal of Computational and Applied Mathematics 352, 210-222, 2019 | 9 | 2019 |
| Asymptotic theory for LAD estimation of moderate deviations from a unit root Z Zhiyong, L Zhengyan Statistics & Probability Letters 90, 25-32, 2014 | 7 | 2014 |
| A unified framework for constructing nonconvex regularizations Z Zhou IEEE Signal Processing Letters 29, 479-483, 2022 | 6 | 2022 |
| Varying coefficient partially nonlinear models with nonstationary regressors Z Zhiyong, L Zhengyan Journal of Statistical Planning and Inference 194, 47-64, 2018 | 6 | 2018 |
| Minimization of the q-ratio sparsity with 1< q≤∞ for signal recovery Z Zhou, J Yu Signal Processing 189, 108250, 2021 | 4 | 2021 |
| Sparse recovery based on the generalized error function Z Zhou arXiv preprint arXiv:2105.13189, 2021 | 4 | 2021 |
| Stable and robust -constrained compressive sensing recovery via robust width property Z Zhou, J Yu arXiv preprint arXiv:1705.03810, 2017 | 4 | 2017 |
| Quantile inference for moderate deviations from a unit root model with infinite variance Z Zhiyong, L Zhengyan Journal of the Korean Statistical Society 44, 280-294, 2015 | 3 | 2015 |
| Phaseless compressive sensing using partial support information Z Zhou, J Yu Optimization Letters 14 (7), 1961-1973, 2020 | 2 | 2020 |
| Statistical inference for the block sparsity of complex‐valued signals J Wang, Z Zhou, J Yu IET Signal Processing 14 (3), 154-161, 2020 | 2 | 2020 |
| Error bounds of block sparse signal recovery based on q-ratio block constrained minimal singular values J Wang, Z Zhou, J Yu EURASIP Journal on Advances in Signal Processing 2019, 1-12, 2019 | 2 | 2019 |
| Block sparse signal recovery via minimizing the block q-ratio sparsity Z Zhou Journal of Computational and Applied Mathematics 438, 115566, 2024 | 1 | 2024 |
| Bayesian sparsity estimation in compressive sensing with application to MR images J Wang, Z Zhou, A Garpebring, J Yu Communications in Statistics: Case Studies, Data Analysis and Applications 5 …, 2019 | 1* | 2019 |
Jun YuProfessor in Mathematical Statistics, Umeå University and SLU, Umeå, Sweden在 umu.se 的电子邮件经过验证
Jun YuProfessor in Mathematical Statistics, Umeå University and SLU, Umeå, Sweden在 umu.se 的电子邮件经过验证
Zhengyan LinProfessor of Mathematics, Zhejiang University在 zju.edu.cn 的电子邮件经过验证