点击下载
1.Zhang, H., Sun, L., Zhou, Y. and Huang, J. (2017). Oracle inequalities and selection consistency for weighted lasso in high-dimensional additive hazards model. Statistica Sinica, 27, 1903-1920.
点击下载
2.Zhang, H., Wang, D. and Sun, L. (2017). Regularized estimation in GINAR(p) process. Journal of the Korean Statistical Society, In press.
点击下载
3.Zhou, J., Zhang, H., Sun, L. and Sun, J. (2017). Joint analysis of panel count data with informative observation process and a dependent terminal event. Lifetime Data Analysis, In press.
点击下载
4.Fang, S., Zhang, H., Sun, L. and Wang, D. (2017). Analysis of panel count data with time-dependent covariates and informative observation process. Acta Mathematicae Applicatae Sinica(English Series), 33, 147-156.
点击下载
5.Yoon, G., Zheng, Y., Zhang, Z., Zhang, H., Gao, T., Joyce, B., Zhang, W., Guan, W., Baccarelli, A., Jiang, W., Schwartz, J., Vokonas, P., Hou, L. and Liu, L.(2017). Ultra-high dimensional variable selection with application to normative aging study: DNA methylation and metabolic syndrome. BMC Bioinformatics, 18:156.
6.Zhang, H., Zheng, Y., Yoon, G., Zhang, Z., Gao, T., Joyce, B., Zhang, W., Schwartz, J., Vokonas, P., Colicino, E., Baccarelli, A., Hou, L. and Liu, L. (2017). Regularized estimation in sparse highdimensional multivariate regression, with application to a DNA methylation study. Statistical Applications in Genetics and Molecular Biology, 16, 159 - 171.
7.Wang, X, Wang, D. and Zhang, H. (2017). Poisson autoregressive process modeling via the penalized conditional maximum likelihood procedure. Statistical Papers. In press.
8.Zhang, H., Zheng, Y., Zhang, Z., Gao, T., Joyce, B., Yoon, G., Zhang, W., Schwartz, J., Just, A., Colicino, E., Vokonas, P., Zhao, L., Lv, J., Baccarelli, A.,Hou, L. and Liu, L. (2016). Estimating and testing high-dimensional mediation effects in epigenetic studies. Bioinformatics, 32, 3150–3154
9.Fang, S., Zhang, H. and Sun, L. (2016). Joint analysis of longitudinal data with additive mixed effect model for informative observation times. Journal of Statistical Planning and Inference,169, 43-55.
10.Liu, Y., Wang, D., Zhang, H. and Shi, N. (2016). Bivariate zero truncated Poisson INAR(1) process. Journal of the Korean Statistical Society.45, 260-275
11.Li, C., Wang, D. and Zhang, H. (2015). First-order mixed integer-valued autoregressive processes with zero-inflated generalized power series innovations. Journal of the Korean Statistical Society, 44, 232-246.
12.Zhang, H. and Wang, D. (2015). Inference for random coefficient INAR(1) process based on frequency domain analysis, Communications in Statistics: Simulation and Computation, 44, 1078-1100.
13.Jia, B., Wang, D. and Zhang, H.(2014). A study for missing values in PINAR(1) processes. Communications in Statistics: Theory and Methods, 43, 4780-4789.
14.Zhang, H., Wang, D. and Zhu, F. (2012). Generalized RCINAR(1) process with signed thinning operator. Communications in Statistics: Theory and Methods,41, 1750-1770.
15.Zhang, H., Zhao, H., Sun, J., Wang, D. and Kim, K. (2013). Regression analysis of multivariate panel count data with an informative observation process. Journal of Multivariate Analysis, 119, 71-80.
16.Zhang, H., Sun, J. and Wang, D. (2013). Variable selection and estimation for multivariate panel count data via the seamless-L0 penalty. The Canadian Journal of Statistics, 41, 368-385.
17.Zhang, H., Wang, D. and Zhu, F. (2011). Empirical likelihood inference for random coefficient INAR(p) process. Journal of Time Series Analysis, 32, 195-203.
18.Zhang, H., Wang, D. and Zhu, F. (2011). The empirical likelihood for first-order random coefficient integer-valued autoregressive processes. Communications in Statistics: Theory and Methods, 40, 492-509.
19.Wang, D. and Zhang, H. (2011). Generalized RCINAR(p) process with signed thinning operator. Communications in Statistics: Simulation and Computation, 40, 13-44.
20.Zhang, H., Wang, D. and Zhu, F. (2010). Inference for INAR(p) processes with signed generalized power serie thinning operator. Journal of Statistical Planning and Inference, 140, 667-683.
