| On the jump activity index for semimartingales BY Jing, XB Kong, Z Liu, P Mykland Journal of Econometrics 166 (2), 213-223, 2012 | 81 | 2012 |
| Modeling high-frequency financial data by pure jump processes BY Jing, XB Kong, Z Liu | 68 | 2012 |
| WaVPeak: picking NMR peaks through wavelet-based smoothing and volume-based filtering Z Liu, A Abbas, BY Jing, X Gao Bioinformatics 28 (7), 914-920, 2012 | 66 | 2012 |
| On the estimation of integrated volatility with jumps and microstructure noise BY Jing, Z Liu, XB Kong Journal of Business & Economic Statistics 32 (3), 457-467, 2014 | 60 | 2014 |
| Testing for pure-jump processes for high-frequency data XB Kong, Z Liu, BY Jing The Annals of Statistics 43 (2), 847-877, 2015 | 53 | 2015 |
| Automatic peak selection by a Benjamini-Hochberg-based algorithm A Abbas, XB Kong, Z Liu, BY Jing, X Gao PloS one 8 (1), e53112, 2013 | 41 | 2013 |
| High-dimensional covariance matrices in elliptical distributions with application to spherical test J Hu, W Li, Z Liu, W Zhou The Annals of Statistics 47 (1), 527-555, 2019 | 34 | 2019 |
| Model free feature screening for ultrahigh dimensional data with responses missing at random P Lai, Y Liu, Z Liu, Y Wan Computational Statistics & Data Analysis 105, 201-216, 2017 | 34 | 2017 |
| Novel hybrid extreme learning machine and multi-objective optimization algorithm for air pollution prediction L Bai, Z Liu, J Wang Applied Mathematical Modelling 106, 177-198, 2022 | 28 | 2022 |
| Estimating the jump activity index under noisy observations using high-frequency data BY Jing, XB Kong, Z Liu Journal of the American Statistical Association 106 (494), 558-568, 2011 | 27 | 2011 |
| Some properties of finite-time stable stochastic nonlinear systems J Yin, D Ding, Z Liu, S Khoo Applied Mathematics and Computation 259, 686-697, 2015 | 24 | 2015 |
| Regularized estimation for the least absolute relative error models with a diverging number of covariates X Xia, Z Liu, H Yang Computational Statistics & Data Analysis 96, 104-119, 2016 | 21 | 2016 |
| A test for equality of two distributions via jackknife empirical likelihood and characteristic functions Z Liu, X Xia, W Zhou Computational Statistics & Data Analysis 92, 97-114, 2015 | 19 | 2015 |
| Estimating spot volatility in the presence of infinite variation jumps Q Liu, Y Liu, Z Liu Stochastic Processes and their Applications 128 (6), 1958-1987, 2018 | 17 | 2018 |
| Disentangling the effect of jumps on systematic risk using a new estimator of integrated co-volatility K Wang, J Liu, Z Liu Journal of Banking & Finance 37 (5), 1777-1786, 2013 | 17 | 2013 |
| Identification of metabolite markers associated with kidney function H Peng, X Liu, C Aoieong, T Tou, T Tsai, K Ngai, HI Cheang, Z Liu, P Liu, ... Journal of Immunology Research 2022 (1), 6190333, 2022 | 15 | 2022 |
| Balanced augmented jackknife empirical likelihood for two sample U-statistics C Cheng, Y Liu, Z Liu, W Zhou Science China Mathematics 61, 1129-1138, 2018 | 13 | 2018 |
| Estimating the integrated volatility using high-frequency data with zero durations Z Liu, XB Kong, BY Jing Journal of Econometrics 204 (1), 18-32, 2018 | 12 | 2018 |
| Forecasting realized volatility with machine learning: Panel data perspective H Zhu, L Bai, L He, Z Liu Journal of Empirical Finance 73, 251-271, 2023 | 11 | 2023 |
| Linear kernel tests via empirical likelihood for high-dimensional data L Ding, Z Liu, Y Li, S Liao, Y Liu, P Yang, G Yu, L Shao, X Gao Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 3454-3461, 2019 | 11 | 2019 |
Zhou WangNational University of SingaporeVerified email at nus.edu.sg
