科研成果

  • 2005至今代表性论文(*为通讯作者):

【接收待发表】

  • 64. Qingzhi Zhong, Wei Liu, Li Liu, Hua Liang and Huazhen Lin*,Generalized functional feature regression models.Statistica Sinica. Online.

  • 63. Jiaxin Liu#, Hongliang Liu#, Yi Li and Huazhen Lin*,A Combined Moment Equation Approach for Spatial Autoregressive Models.The Canadian Journal of Statistics. Online.

  • 62. Guizhen Li#, Mengying You#, Ling Zhou, Hua Liang andHuazhen Lin*, A projection-based diagnostic test for generalized functional regression models. Statistica Sinica. Online.

  • 61. Wei Liu, Huazhen Lin*, Jin Liu and Shurong Zheng. Two-directional simultaneous inference for high-dimensional models. Journal of Business & Economic Statistics.Online.

【2023年】

  • 60. Chenlin Zhang, Huazhen Lin*, Li Liu, Jin Liu and Yi Li (2023). Functional data analysis with covariate-dependent mean and covariance structures.Biometrics. 79, 2232-2245.

  • 59. Ye He#, Ling Zhou#, Yingcun Xia and Huazhen Lin*(2023).Centre-augmented L2-type regularization for subgroup learning.Biometrics. 79, 2157-2170.

  • 58. Wei Liu, Huazhen Lin*, Li Liu, Yanyuan Ma, Ying Wei and Yi Li (2023). Supervised structural learning of semiparametric regression on high-dimensional correlated covariates with applications to eQTL studies. Statistics in Medicine. 42,3145-3163.

  • 57. Huazhen Lin, Shuangxue Zhao, Li Liu*, Wenyang Zhang(2023). Structured Ultrahigh Dimensional Multiple-Index Models with Efficient Estimation in Computation and Theory. Statistica Sinica. 33, 2137-2160.

  • 56. Wei Liu, Huazhen Lin*, Shurong Zheng and Jin Liu(2023). Generalized factor model for ultra-high dimensional correlated variables with mixed types. Journal of the American Statistical Association.118, 1385-1401.

【2022年】

  • 55. Wei Liu, Xu Liao, Yi Yang, Huazhen Lin, Joe Yeong, Xiang Zhou*, Xingjie Shi* and Jin Liu* (2022). Joint dimension reduction and clustering analysis of single-cell RNA-seq and spatial transcriptomics data. Nucleic Acids Research.50, e72.

  • 54. Mengyin You, Huazhen Lin* and Hua Liang(2022). Dynamically integrated regression model for online auction data. Science China.66(7)1531-1552

  • 53. Huazhen Lin*, Jiaxin Liu, Haoqi Li, Lixian Pan and Yi Li (2022). Effcient Estimation and Computation in Generalized Varying Coeffcient Models with Unknown Link and Variance Functions for Large-Scale Data. Statistica Sinica.32, 847-868.

  • 52. Jiakun Jiang, Huazhen Lin*, Qingzhi Zhong and Yi Li (2022). Analysis of multivariate non-gaussian functional data: a semiparametric latent process approach.Journal of Multivariate Analysis. 189,104888.

  • 51. Jiakun Jiang, Huazhen Lin*, Heng Peng, Gang-Zhi Fan, and Yi Li (2022). Cluster Analysis with Regression of Non-Gaussian Functional Data on Covariates.Canadian Journal of Statistics.50,221-240.

【2021年】

  • 50. Qinzhi Zhong, Huazhen Lin* and Yi Li (2021). Cluster Non-Gaussian Functional Data. Biometrics. 77, 852-865.

  • 49. Huazhen Lin*, Wei Liu and Wei Lan (2021). Regression analysis with individual-specific patterns of missing covariates. Journal of Business & Economic Statistics. 39, 179-188.

  • 48. Huazhen Lin, Jiakun Jiang, Binhuan Wang* and Paul S. F. Yip (2021). A threshold varying-coefficient autoregressive model for analyzing the influence of media reports of suicide on the actual suicides. Statistica Sinica. 31, 361-390.

【2019年】

  • 47. Kevin He, Jian Kang, Hyokyoung G.Hong, Ji Zhu,Yanming Li, Huazhen Lin, Han Xu, Yi Li* (2019). Covariance-insured screening. Computational Statistics and Data Analysis, 132, 100-114.

  • 46. Huazhen Lin, Hyokyoung G. Hong, Baoying Yang, Wei Liu, Yong Zhang, Gang-Zhi Fan, Yi Li* (2019). Nonparametric time-varying coefficient models for panel data: Study of collection rate of public pension contributions. Statistics in Biosciences. 11,548-566.

  • 45. Huazhen Lin*, Baoying Yang, Ling Zhou, Paul S. F. YIP, Ying-Yeh Chen and Hua Liang (2019). Global kernel estimator and test of varying-coefficient autoregressive model. Canadian Journal of Statistics. 47, 487–519.

  • 44. Ling Zhou, Huazhen Lin*, Kani Chen and Hua Liang(2019). Efficient estimation and computation of parameters and nonparametric functions in generalized semi/non-parametric regression models. Journal of Econometrics. 213,593-607.

  • 43. Haoqi Li, Huazhen Lin*, Paul S. F. Yip and Yuan Li (2019). Estimating population size of heterogeneous populations with large data sets and a large number of parameters. Computational Statistics and Data Analysis. 139,34-44.

  • 42. Ling Zhou, Haoqi Li, Huazhen Lin and Peter X.-K. SONG* (2019). Evaluating functional covariate-environment interactions in the Cox regression model. Canadian Journal of Statistics. 47, 204–221.

  • 41. Xuerong Chen, Haoqi Li, Hua Liang and Huazhen Lin* (2019). Functional response regression analysis. Journal of Multivariate Analysis. 169, 218-233.

【2018年】

  • 40. Ling Zhou, Huazhen Lin* and Hua Liang (2018). Efficient estimation of the nonparametric mean and covariance functions for longitudinal and sparse functional data. Journal of the American Statistical Association,113, 1550-1564.

  • 39. Shaogao Lv, Jiakun Jiang, Fanyin Zhou, Jian Huang and Huazhen Lin*(2018).Estimating High-Dimensional Additive Cox Model Withtime-Dependent Covariate Processes.Scandinavian Journal of Statistics, 45,900-922

  • 38. Ye He, Huazhen Lin* and Dongsheng Tu (2018). A single-index threshold Cox proportional hazard model for identifying treatment-sensitive subset based on multiple biomarkers.Statistics in Medicine, 37,3267-3279.

  • 37. Huazhen Lin*, Fanyin Zhou, Qiuxia Wang, Ling Zhou and Jing Qin(2018). Robust and efficient estimation for the treatment effect in causal inference and missing data problems.Journal of Econometrics,205,363-380.

  • 36. Shaogao Lv, Mengying You, Huazhen Lin*, Heng Lian and Jian Huang (2018). On the sign consistency of the Lasso for the high-dimensional Cox model.Journal of Multivariate Analysis,167,79-96.

  • 35. Shaogao Lv, Huazhen Lin*, Heng Lian and Jian Huang (2018). Oracle Inequalities for Sparse Additive Quantile Regression in Reproducing Kernel Hilbert Space.The Annals of Statistics, 46,781–813.

  • 34. Huazhen Lin*, Lixian Pan, Shaogao Lv and Wenyang Zhang (2018). Efficient Estimation and Computation for the Generalized Additive Models with Unknown Link Function.Journal of Econometrics,202, 230–244.

【2017年】

  • 33. Yunbei Ma, Yi Li, Huazhen Lin* and Yi Li (2017). Concordance measure-based feature screening and variable selection.Statistica Sinica, 27, 1967-1985.

  • 32.Huazhen Lin*, Ling Zhou and Binhuan Wang (2017). Generalized partial linear models with unknown link and unknown baseline functions for longitudinal data.Statistica Sinica, 27, 1281-1298.

【2016年】

  • 31.Huazhen Lin*, Zhe Fei and Yi Li (2016). A semiparametrically efficient estimator of the time-varying effects for survival data with time-dependent treatment.Scandinavian Journal of Statistics, 43, 649-663.

  • 30. Kevin He, Yanming Li, Ji Zhu, Hongliang Liu, Jeffrey E. Lee, Christopher I. Amos, Terry Hyslop, Jiashun Jin, Huazhen Lin, Qinyi Wei and Yi Li* (2016). Component-wise gradient boosting and false discovery control in survival analysis with high-dimensional covariates.Bioinformatics, 32, 50-57.

  • 29. 蒋家坤,林华珍*,蒋靓, Paul S. F. YIP (2016).门槛回归模型中门槛值和回归参数的估计,《中国科学》,46,409-422.

  • 28. Huazhen Lin*, Ming T. Tan and Yi Li (2016). A Semiparametrically Efficient Estimator of Single-index Varying Coefficient Cox Proportional Hazards Models.Statistica Sinica, 26, 779-807.

  • 27. Huazhen Lin*, Ye He and Jian Huang (2016). A global partial likelihood estimation in the additive Cox proportional hazards model,Journal of Statistical Planning and Inference, 169, 71-87.

  • 26. Ling Zhou, Huazhen Lin and Yi-Chen Lin* (2016). Education, Intelligence, and Well-Being: Evidence from a Semiparametric Latent Variable Transformation Model for Multiple Outcomes of Mixed Types.Social Indicators Research, 125, 1011-1033.

【2015年】

  • 25. 李好奇,林华珍,张兴凤,朱玉峰,张伟(2015).基于混合效应模型的医保费用测算及监控,《数理统计与管理》,34,1040-1047。

【2014年】

  • 24. Huazhen Lin, Ling Zhou and Xiao-Hua Zhou* (2014). Semiparametric regression analysis of longitudinal skewed data.Scandinavian Journal of Statistics, 41, 1031–1050.

  • 23. Ling Zhou, Huazhen Lin*, Xin-Yuan Song and Yi Li (2014). Selection of latent variables for multiple mixed-outcome models.Scandinavian Journal of Statistics, 41,1064-1082.

  • 22. Huazhen Lin*, Ling Zhou, Chunhong Li and Yi Li (2014). Semiparametric transformation models for semicompeting survival data.Biometrics, 70, 599-607.

  • 21. Huazhen Lin*, Ling Zhou, Robert M. Elashof and Yi Li (2014). Semiparametric latent variable transformation models for multiple mixed outcomes.Statistica Sinica, 24 , 833-854.

  • 20. Huazhen Lin*, Yi Li, Liang Jiang and Gang Li(2014). A semiparametric linear transformation model to estimate causal effects for survival data.Canadian Journal of Statistics, 42, 18-35.

【2013年】

  • 19.Huazhen Lin* and Jianxin Pan (2013). Nonparametric estimation of mean and covariance structures for longitudinal data.Canadian Journal of Statistics, 41,557-574.

  • 18. 罗荣华*,林华珍,翟立宏(2013).银行理财产品收益率曲线的构建与分析—基于随机效应半参数模型的方法,《金融研究》,7:107-120。

  • 17. Huazhen Lin*,Yi Li and Ming T. Tan (2013). Estimating a unitary effect summary based on combined survival and quantitative outcomes.Computational Statistics and Data Analysis, 66, 129-139.

  • 16. Huazhen Lin and Heng Peng* (2013). Smoothed rank correlation of the linear transformation regression model.Computational Statistics and Data Analysis, 57, 615-630.

【2012年】

  • 15. Huazhen Lin, Xiao-hua Zhou* and Gang Li (2012). A Direct Semiparametric Receiver Operating Characteristic Curve Regression with Unknown Link and Baseline Functions.Statistica Sinica, 22,1427-1456.

  • 14. Kani Chen, Huazhen Lin* and Yong Zhou (2012). Efficient estimation for the Cox model with varying coefficients.Biometrika, 2, 379-392.

【2011年】

  • 13. Huazhen Lin*, Paul S. F. Yip and Richard M. Huggins (2011). A Nonparametric Estimation of the Infection Curve.Science China, 54, 1815-1828.

  • 12. Huazhen Lin, Ling Zhou, Heng Peng and Xiao-Hua Zhou* (2011). Selection and combination of biomarkers using ROC method for disease classification and prediction.Canadian Journalof Statistics,39, 324-343.

【2010年】

  • 11. Huazhen Lin and Peter X.-K. Song* (2010). Longitudinal Semiparametric Transition Models with Unknown Link and Variance Functions,Statistics and Its Interface, 3, 197-209.

  • 10. Huazhen Lin, Danping Liu and Xiao-Hua Zhou* (2010). A correlated random effects model for longitudinal data with nonignorable missingness,Statistics in Medicine, 29, 236-247.

【2009年】

  • 9. Huazhen Lin and Xiao-Hua Zhou*(2009). A semi-parametric two-part mixed-effects heteroscedastic transformation model for correlated right-skewed semi-continuous data.Biostatistics, 10, 640-658.

  • 8. Xiao-Hua Zhou*, Huazhen Lin and Eric Johnson (2009). Nonparametric heteroscedastic transformation regression models for skewed data with an application to health care costs.Journal of the Royal Statistical Society: Series B (Statistical Methodology), 70,1029-1047.

  • 7. Huazhen Lin, Paul S.F.YIP* and Feng Chen(2009). Estimating the Population Size for a Multiple List Problem with an Open Population,Statistica Sinica, 19, 177-196.

【2008年】

  • 6. Xiao-Hua Zhou* and Huazhen Lin(2008). Semi-parametric maximum likelihood estimates for ROC curves of continuous-scale tests.Statistics in Medicine, 10, 5271-5290.

  • 5. Huazhen Lin, Paul S.F.YIP* and Richard M. Huggins(2008). A Double-nonparametric Procedure for Estimating the Number of Delay-reported Cases.Statistics in Medicine, 27, 3325-3339.

【2007年】

  • 4. Huazhen Lin, Peter X.-K. SONG* and Qian M. Zhou (2007). Varying-coefficient generalised linear models for longitudinal data.Sankhya, 69, 582-615.

【2006年】

  • 3. Zhou, X. H.*, Qin, G., Huazhen Lin and Li, G. (2006). Inferences in Censored Cost Regression Models with Empirical Likelihood.Statistica Sinica, 16, 1213-1232.

  • 2. Jianqing Fan, Huazhen Lin and Yong Zhou* (2006). Local partial-likelihood estimation for life time data. The Annals of Statistics, 34, 290-325.

【2005年】

  • 1. Paul S.F.Yip*, Huazhen Lin and Liqun Xi(2005). A Semiparametric Method for Estimating Population Size for Capture-Recapture Experiments with Random Covariates in Continuous Time.Biometrics, 61, 1085-1092.