科研(Research)

科研项目(Research projects)

  • 国家自然科学基金项目"混合多响应纵向数据的均值相关结构同时统计推断方法研究",2017.1- 2020.12
  • 国家自然科学基金项目"纵向数据分析中的有效统计推断方法及其应用",2013.1- 2016.12
  • 国家青年科学基金项目"高通量基因数据分析中的 Bayes 统计方法"2009.1-2011.12
  • 中国科学技术大学青年基金项目"统计推断中的Bayes分析及其应用",2006.1-2008.12

STATISTICS PUBLICATIONS
  • Gong T., Zhang W., Chen Y. (2023) Uncovering block structures in large rectangular matrices, Journal of Multivariate Analysis. 198, 105211
  • Zhang W, Li Y, Chen Y, Tang C Y. (2023) Parsimonious Gaussian copula modelling through constrained Cholesky decomposition for data with temporal dependence (in Chinese). Sci Sin Math, 2023, 53(5): 777–790.
  • Shu L., Chen Y., Zhang W. and Wang X. (2022) Spatial rank-based high-dimensional change point detection via random integration. Journal of Multivariate Analysis 189, 104942
  • Zhang W., Zhao D., Chen Y} (2022) Regression Estimation for longitudinal Data with Nonignorable Intermittent Nonresponse and Dropout. Communications in Mathematics and Statistics.10, 383–411.
  • Chen Y., Wang J., Zhang W. (2022) Tail distortion risk measure for portfolio with multivariate regularly variation. Communications in Mathematics and Statistics,10, 263–285
  • Zhang, W. Jin B., Bai Z. (2021) Learning Block Structure in U-statistic Based Large Correlation Matrices. Biometrika, 108(4):933-946.
  • Qian F, Zhang, W. Chen Y. (2021) Adaptive Banding Covariance Estimation for High-dimensional Multivariate Longitudinal Data. The Canadian Journal of Statistics, 49(3):906-938
  • Hu J., Chen Y., Zhang, W. Guo X. (2021) Penalized high-dimensional M-quantile regression: From L1 to Lp optimization. The Canadian Journal of Statistics, 49(3):875-905
  • Chen Y., Hu J. and Zhang, W. * (2020) Too Connected to Fail? Evidence from a Chinese Financial Risk Spillover Network , China \& World Economy, 28(6):78-100.
  • Zhang, W. *, Xie, F. and Tan J. (2020) A Robust Joint Modeling Approach for Longitudinal Data with Informative Dropouts, Computational Statistics, 35(4), 1759-1783
  • Zhao W., Zhang W. \& Lian H. (2020) Marginal quantile regression for varying coefficient models with longitudinal data, Ann Inst Stat Math. 72:213–234
  • Zhang, F., Zhang, W. , Li, R., Lian, H. (2020) Faster convergence rate for functional linear regression in reproducing kernel Hilbert spaces, Statistics, 54:167-181.
  • Zhang W. , Wang J., Qian F., Chen Y.* (2020) A joint mean-correlation modeling approach for longitudinal zero-inflated count data, Brazilian Journal of Probability and Statistics, 34(1):35–50.
  • Qian F., Chen Y., Zhang, W. (2020) Regularized Estimation of Precision Matrix for High-dimensional Multivariate Longitudinal Data. Journal of Multivariate Analysis, 176:104580.
  • Zhang, W. , Zhang M. and Chen Y*. (2020) A copula-based GLMM model for multivariate longitudinal data with mixed-types of responses, Sankhya B, Volume 82-B, Part 2, pp. 353-379
  • Cui Di, Zhang Weiping. Bayesian variable selection for proportional hazards model with current status data[J]. Journal of University of Science and Technology of China, 2020, 50(10): 1303-1314.
  • LI Yezhen, ZHANG Weiping. A Cholesky factor model in correlation modeling for discrete longitudinal data[J]. Journal of University of Science and Technology of China, 2020, 50(9): 1266-1276.
  • Tan Jiaxin, Zhang Weiping. A robust joint modeling approach for longitudinal data.Journal of University of Science and Technology of China,2020,50(03):317-327+348.
  • Tang, C. Y., Zhang, W. and Leng, C. (2019) Discrete longitudinal data modeling with a mean-correlation regression approach. Statistica Sinica, 29:853-876.
  • Lin H., Jiang X., Lian H., Zhang, W. (2019) Reduced rank modeling for functional regression with functional responses. Journal of Multivariate Analysis, Vol. 169(01)205-217.
  • Chen, Y., Gao, Y., Gao, W. Zhang, W. (2018) Second-Order Asymptotics of the Risk Concentration of a Portfolio with Deflated Risks, Mathematical Problems in Engineering, 1-12.
  • Zhang, W., Leng, C., and Tang, C. Y. (2015). A joint modeling approach for longitudinal studies. Journal of the Royal Statistical Society Series B, 77(1):219–238.
  • Zhang, W.,Liu, Y. and Li， R. (2015). Joint Modeling of Mean-Covariance Structures Based on Partial Autocorrelation for Longitudinal Data. Journal of Applied Probability and Statistics,31(6): 582-595..
  • Liu M. and Zhang, W. .Bayesian Joint Semiparametric Mean-Covariance Modeling for Longitudinal Data.
  • Leng, C. and Zhang, W. (2014). Smoothing combined estimating equations in quantile regression for longitudinal data. Statistics and Computing, 24(1):123-136.
  • Xing, X., Liu, M. and Zhang, W. (2013). Joint Semiparametric Mean-Covariance modeling by Moving Average Cholesky Decomposition for Longitudinal Data, Journal of University of Science and Technology of China, 43(8):607-621.
  • Liu, X. and Zhang, W. (2013). A moving average Cholesky factor model in joint mean-covariance modeling for longitudinal data, Science in China A, Vol56(11): 2367-2379.
  • Weiping Zhang, Chenlei Leng (2012) A Moving Average Cholesky Factor Model in Covariance Modeling for Longitudinal Data, Biometrika, 99:141-150.
  • Weiping Zhang, Laisheng Wei, Yu Chen (2012), The Superiorities of Bayes Linear Unbiased Estimator in Multivariate Linear Models, Acta Mathematica Applicatae Sinica, 28(2), 383:394.
  • Weiping Zhang, Laisheng Wei, Yu Chen (2011), The Superiorities of Bayes Linear Unbiased Estimation in Partitioned Linear model, Jrl Syst Sci & Complexity, 24: 945–954
  • Chenlei Leng,Weiping Zhang, Jianxin Pan (2010), Semiparametric Mean-Covariance Regression Analysis for Longitudinal Data, Journal of the American Statistical Association, Vol.105, No. 489: 181-193.
  • Weiping Zhang, Yu Chen (2010) , Double Sampling Method for Genetic Association Analysis with Differential Genotyping Errors, Biostatistics, Bioinformatics and Biomathematics, Vol1,43-54.
  • Chen, Yu; Zhang, Weiping; and Liu, Jie (2010) Asymptotic Tail Probability of Randomly Weighted Sum of Dependent Heavy-Tailed Random Variables, Asia-Pacific Journal of Risk and Insurance: Vol.4 :Iss. 2.
  • Weiping Zhang, Nien-fan Zhang, Hung-kung Liu (2009), A generalized method for the multiple artifacts problem in interlaboratory comparisons with linear trends, Metrologia, 46:345-350.
  • Z. Q. J. Lu, N. D. Lowhorn, W. Wong-Ng, W. Zhang, et. al. (2009) Statistical Analysis of a Round-Robin Measurement Survey of Two Candidate Materials for a Seebeck Coefficient Standard Reference Material, J. Res. Natl. Inst. Stand. Technol. 114, 37-55.
  • Lowhorn, N.D.; Wong-Ng, W.; Zhang, W.; Lu, Z.Q.; Otani, M.; et al. (2009) Round-robin measurements of two candidate materials for a Seebeck coefficient Standard Reference Material, Applied Physics A: Materials Science & Processing,Vol94, No.2:231-234
  • Zhang Weiping, Wei Laisheng (2008), The superiority of empirical Bayes estimation of parameters in partitioned normal linear model, Acta. Math. Scientia, 28B(4):955-962.
  • 霍涉云, 张伟平, 韦来生 (2007), 一类线性模型参数的Bayes估计及其优良性, 中国科技大学学报, 37(7): 773-776
  • 张伟平, 韦来生 (2007), 错误先验假定下Bayes线性无偏估计的稳健性, 应用概率统计, 23(1): 59-67.
  • Laisheng Wei, Weiping Zhang (2007), The Superiorities of Bayes Linear Minimum Risk Estimation in Linear Model, Communication in statistics—theory and methods, 36(5): 917-926.
  • Yu Chen, Weiping Zhang (2007), Large deviations for random sums of negatively dependent random variables with consistently varying tails. Statist.Prob.Lett.77, 530-538.
  • 张伟平, 韦来生 (2005), 单向分类随机效应模型中方差分量的渐近最优经验Bayes估计, 系统科学与数学, 25(1): 106-117.
  • Wei Laisheng, Zhang Weiping (2005), Empirical Bayes test problems of variance components in random effect model, Acta. Math. Scientia, 25B(2):274-282..
  • Zhang Weiping, Wei Laisheng, Yang Yaning (2005), The superiority of empirical Bayes estimator of parameters in linear model, Statist. Prob. Lett. 72:43-50.
  • Zhang Weiping, Wei Laisheng (2005), On Bayes linear unbiased estimation of estimable functions for the singular linear model, Sci. in China, A. 48(7): 898-903.

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