林路 教授

山东大学中泰证券金融研究院教授，博士生导师

[63] Xiaoyu Ma, Lu Lin and Yujie Ga. (2023). A general framework of online updating variable selection for generalized linear models with streaming datasets. Journal of Statistical Computation and Simulation. 93, 325 -- 340.

[62] Qinqin Hu, Lu Lin. (2022). Feature Screening in High Dimensional Regression with Endogenous Covariates. Computational Economics, 60:949 – 969.

[61] 孙晓霏, 王康宁, 李劭珉, 林路. (2022). 基于众数回归的纵向数据部分线性模型的稳健经验似然及变量选择. 中国科学 : 数学, 第 52 卷 第 4 期 : 447 -- 466.

[60] Lu Lin, Lili Liu, Xia Cui and Kangning Wang. (2021). A Generalized Semiparametric Regression and Its Efficient Estimation. Scandinavian journal of statistics. 48,1-24.

[59] Jun Lu, Lu Lin, WenWu Wang. (2021). Partition-based feature screening for categorical data via RKHS Embeddings. Computational Statistics and Data analysis. 157, 107176.

[58] Lili Liu,Mae Gordon, J. Philip Miller, Michael Kass, Lu Lin,Shujie Ma, Lei Liu.(2021). Capturing heterogeneity in repeated measures data by fusion penalty. Statistics in Medicine. 40,1901–1916.

[57] 王康宁, 李劭珉, 林路. (2020). 基于copula 函数的纵向数据复合分位数回归及变量选择. 中国科学: 数学2020 年第50 卷第8 期: 1097-1116.

[56] Jiandong Shi, Dehui Luo, Hong Weng, Xian-Tao Zeng, Lu Lin, Haitao Chu and Tiejun Tong. (2020). Optimally estimating the sample standard deviation from the five‐number summary. Research Synthesis Methods. 11, 642-654.

[55] Yongxin, Liu and Lu Lin. (2019). Classification with minimum ambiguity under distribution heterogeneity. Journal of Statistical Computation and Simulation. 89, 2239–2260.

[54] Wang. W. W., Yu, P., Lin, L. and Tong, T. J. (2019). Robust Estimation of Derivatives Using Locally Weighted Least Absolute Deviation Regression. Journal of Machine Learning Research. 20, 1-49

[53] Lu Lin, Feng Li, Kangning Wang and Lixing Zhu. (2019). Composite estimation: An asymptotically weighted least squares approach. Statistica Sinica 29, 1367-1393.

[52] Hai tao, Liu Lei, Jiang Hongmei and Lin Lu. (2018).A Marginalized Two-Part Beta Regression Model for Microbiome Compositional Data. PLOS Computational Biology. 14(7): e1006329.

[51] Ping Dong, Lu Lin and Yunquan Song. (2018). Significance test of clustering under high dimensional setting with applications to cancer data. Journal of Statistical Computation and Simulation. 88:17, 3349-3378.

[50] Jun Lu and Lu Lin. (2018). Feature screening for multi-response varying coefficient models with ultrahigh dimensional predictors. Computational Statistics and Data Analysis. 128 242–254.

[49] Xiuli Wang, Yunquan Song, Lu Lin. (2017). Handling estimating equation with nonignorably missing data based on SIR algorithm. Journal of Computational and Applied Mathematics. 326, 62-70.

[48]WenWu Wang, Ping Yu, Lu Lin, Tiejun Tong. (2017). Robust Estimation of Derivatives Using Locally Weighted Least Absolute Deviation Regression. Journal of Machine Learning Research 1.1-48.

[47] Lin, L. Liu, Y X, Lin, C. (2017). Mini-max-risk and mini-mean-risk inferences for a partially piecewise regression. Statistics, 51, 745-765.

[46] Qinqin Hu, Lu Lin. (2017). Conditional sure independence screening by conditional marginal empirical likelihood. Annals of the Institute of Statistical Mathematics, 69, 63–96.

[45] Lu Lin, Ping Dong, Yunquan Song and Lixing Zhu. (2017). Upper Expectation Parametric Regression. Statistica Sinica. 27, 1265-1280.

[44] Yunquan Song, Ping Dong, Xiuli Wang and Lu Lin. (2017). Rapid penalized likelihood-based outlier detection via heteroskedasticity test. Journal of Statistical Computation and Simulation. 87:6, 1206-1229.

[43] Wenwu Wang, Lu Lin, Li Yu. (2017). Optimal variance estimation based on lagged second-order difference in nonparametric regression. Comput Stat. 32:1047-1063.

[42] Kangning Wang, Lu Lin. (2017). Robust and efficient direction identification for a groupwise additive multiple-index models and its applications. TEST. 26:22-45.

[41] 盖玉洁, 李锋, 尹钊, 林路, 朱力行（2017）. 自适应的 Dantzig 选择器的渐近性质研究. 中国科学：数学，2017年，第47卷，第7期：869~886.

[40] Lu Lin, Lixing Zhu,Yujie Gai. (2016). Inference for biased models: A quasi-instrumental variable approach. Journal of Multivariate Analysis. 145, 22–36.

[39] Kangning Wang, Lu Lin (2016). Robust structure identification and variable selection in partial linear varying coefficient models. Journal of Statistical Planning and Inference. 174, 153-168.

[37] 林晨，张齐，张晓灵，林路 (2016). 变量约束工具变量回归及其在期权定价和投资组合中的应用. 中国科学: 数学，第46卷，第1 期: 1-16.

[36] 王康宁, 林路. 空间非参回归的变量选择. 中国科学: 数学2016 年第46 卷第3 期: 301-320.

[35] Lu Lin and Jing Sun. (2016). Adaptive conditional feature screening. Computational Statistics and Data Analysis 94, 287–301.

[34] Lu Lin, Yufeng, Shi, Xin Wang and Shuzhen Yang. (2016). k-sample upper expectation linear regression. Journal of Statistical Planning and Inference, 170, 15–26.

[33] Kangning Wang and Lu Lin (2015). Simultaneous structure estimation and variable selection in partial linear varying coefficient models for longitudinal data. Journal of Statistical Computation and Simulation. 85: 1459-1473.

[32] Wenwu Wang and Lu Lin. (2015). Derivative Estimation Based on Difference Sequence via Locally Weighted Least Squares Regresson. Journal of Machine Learning Research. 16, 2617-2641

[31] Xuehu Zhu, Xu Guo, Lu Lin and Lixing Zhu. (2015). Heteroscedasticity Checks for Single Index Models. Journal of Multivariate Analysis. 136, 41–55.

[30] Qinqin Hu, Peng Zeng, Lu Lin. (2015). The dual and degrees of freedom of linearly constrained generalized lasso. Computational Statistics and Data Analysis, 86, 13-26.

[29] Lu Lin, Yunquan Song, Zhao Liu. (2014). Local linear-additive estimation for multiple nonparametric regressions. Journal of Multivariate Analysis. 123, 252–269.

[28] Kangning Wang, Lu Lin. (2014). New efficient estimation and variable selection in models with single-index structure. Statistics and Probability Letters.89, 58-64.

[27] Jing Sun and Lu Lin. (2013). Local rank estimation and related test for varying-coefficient partially linear models. Journal of Nonparametric Statistics. 26, 187-206.

[26] Yujie Gai, Lixing Zhu and Lu Lin. (2013). Model selection consistency of Dantzig selector. Statistica Sinica 23, 615-634.(SCI)

[25] Lu Lin, Jing Sun and Lixing Zhu. (2013). Nonparametric feature screening. Computational Statistics & Data Analysis. 67, 162–174. [24] Xiuli Wang, Fang Chen and Lu Lin. (2013). Empirical likelihood inference for stimating equation with missing data. Science China, Mathematics, 56, 1233-1245.

[23] Jing Su, Yujie Gai, Lu Lin. (2013). Weighted local linear composite quantile estimation for the case of general error distributions. Journal of Statistical Planning and Inference, 143, 1049-1063.

[22] Feng Li, Lu Lin, Yuxia Su. (2013). Variable selection and parameter estimation for partially linear models via Dantzig selctor, Metrika, 76, 225-238.

[21] Gaorong Li, Lu Lin and Lixing Zhu. (2012). Empirical Likelihood for Varying Coefficient Partially Linear Model with Diverging Number of Parameters. Journal of Multivariate Analysis. 105, 85-111.

[20] Lu Lin, Feng Li, Lixing, Zhu (2011). Simulation-based consistent inference for biased working model of non-sparse high-dimensional linear regression. Journal of Statistical Planningand Inference, 141, 3780–3792.

[19] Lu Lin, Qi Zhang, Feng Li, Xia Cui. (2011) .Simulation-based two-stage estimation for multiple nonparametric regression. Computational Statistics & Data Analysis, 55(3), 1367-1378.

[18] Yujie Gaia, Lu Lin, and Xiuli Wang. (2011). Consistent inference for biased sub-model of high-dimensional partially linear model. Journal of Statistical Planning and Inference. 141, 1888-1898.

[17] Xiuli Wang, Gaorong Li and Lu Lin. (2011). Empirical likelihood inference for semi-parametric varying-coefficient partially linear EV models. Metrika, 73, 171-185.

[16] Zhu, L. Lin, L. Cui, X., Li, G. R. (2010). Bias-corrected empirical likelihood in a multi-link semiparametric model. J. Multivariate Anal. 101, 850-868.

[15] Lu Lin, Xia Cui and Lixing Zhu. (2009). An adaptive two-stage estimation method for additive models. Scand. J. Statist. 36, 248-269.

[14] Lixing Zhu, Lu Lin and Qiang Chen. (2009). Adaptive global confidence band for nonparametric regression: an empirical likelihood method. Statistica Sinica, 20, 1771-1787.

[13] Cui，X., Guo, W. S., Lin, L. and Zhu, L. X. (2009). Covariate-adjusted nonlinear regression. Annals of Statistics, 37, 1839-1870.

[12] Lin, L., and Li Feng. (2008). Stable and bias-corrected estimation for nonparametric regression models. Journal of Nonparametric Statistics, 20, 283-303.

[11] Lin, L., Tan, L. (2008). Proper Bayesian estimating equation based on Hilbert Space method. Statistics and Probability Letters. 78,1119-1127.

[10] Lin, L. Fan, Y. Z., Tan, L. (2008) Blockwise bootstrap wavelet in nonparametric regression model with weakly dependent processes. Metrika 67, 31-48.

[9] Yang, G. J., Lin, L. and Zhang, R. C. (2007). Unbiased quasi-regression. China. Ann. Math. 28(B) (2),177-186.

[8] Lin, L. and Cui, X. (2006). Stahel-Donoho kernel estimation for fixed design nonparametric regression models. Science in China Series A: Mathematics, 49(12), 1879-1896.

[7] Lin, L. and Chen, M. H. (2006). Robust Estimating Equation Based on Statistical Depth. Statistical Papers, 47, 263-278.

[6] Lin, L., Zhu, L. X. and Yuen, K. C. (2005) Profile empirical likelihood for parametric and semi-parametric models. Ann. Inst. Statist. Math. 57(3), 485-505.

[5] Lin, L. (2005). Robust depth-weighted wavelet for nonparametric regression models. Acta Mathematica Sinica, English Series, 21, 585 - 592.

[4] Lin, L., Fan, Y. Z., Du, J. and Yuan, Y. (2005). Iterative quasi-likelihood for seemingly unrelated regression systems. Chin. Ann. Math. 26(B) (3),335-346.

[3] Lin, L.(2004). Generalized Quasi likelihood. Statistical Papers, 45,529-544.

[2] Lin, L. (2003). Maximum Information and Optimum Estimating Function. Chin. Ann. Math. 24B, 349-358.

[1] Lin, L. and Zhang, R. C. (2002). Profile Quasi-likelihood. Statistics and Probability Letters. 56, 147-154.

林路 教授

山东大学中泰证券金融研究院教授，博士生导师

[63] Xiaoyu Ma, Lu Lin and Yujie Ga. (2023). A general framework of online updating variable selection for generalized linear models with streaming datasets. Journal of Statistical Computation and Simulation. 93, 325 -- 340.

[62] Qinqin Hu, Lu Lin. (2022). Feature Screening in High Dimensional Regression with Endogenous Covariates. Computational Economics, 60:949 – 969.

[61] 孙晓霏, 王康宁, 李劭珉, 林路. (2022). 基于众数回归的纵向数据部分线性模型的稳健经验似然及变量选择. 中国科学 : 数学, 第 52 卷 第 4 期 : 447 -- 466.

[60] Lu Lin, Lili Liu, Xia Cui and Kangning Wang. (2021). A Generalized Semiparametric Regression and Its Efficient Estimation. Scandinavian journal of statistics. 48,1-24.

[59] Jun Lu, Lu Lin, WenWu Wang. (2021). Partition-based feature screening for categorical data via RKHS Embeddings. Computational Statistics and Data analysis. 157, 107176.

[58] Lili Liu,Mae Gordon, J. Philip Miller, Michael Kass, Lu Lin,Shujie Ma, Lei Liu.(2021). Capturing heterogeneity in repeated measures data by fusion penalty. Statistics in Medicine. 40,1901–1916.

[57] 王康宁, 李劭珉, 林路. (2020). 基于copula 函数的纵向数据复合分位数回归及变量选择. 中国科学: 数学2020 年第50 卷第8 期: 1097-1116.

[56] Jiandong Shi, Dehui Luo, Hong Weng, Xian-Tao Zeng, Lu Lin, Haitao Chu and Tiejun Tong. (2020). Optimally estimating the sample standard deviation from the five‐number summary. Research Synthesis Methods. 11, 642-654.

[55] Yongxin, Liu and Lu Lin. (2019). Classification with minimum ambiguity under distribution heterogeneity. Journal of Statistical Computation and Simulation. 89, 2239–2260.

[54] Wang. W. W., Yu, P., Lin, L. and Tong, T. J. (2019). Robust Estimation of Derivatives Using Locally Weighted Least Absolute Deviation Regression. Journal of Machine Learning Research. 20, 1-49

[53] Lu Lin, Feng Li, Kangning Wang and Lixing Zhu. (2019). Composite estimation: An asymptotically weighted least squares approach. Statistica Sinica 29, 1367-1393.

[52] Hai tao, Liu Lei, Jiang Hongmei and Lin Lu. (2018).A Marginalized Two-Part Beta Regression Model for Microbiome Compositional Data. PLOS Computational Biology. 14(7): e1006329.

[51] Ping Dong, Lu Lin and Yunquan Song. (2018). Significance test of clustering under high dimensional setting with applications to cancer data. Journal of Statistical Computation and Simulation. 88:17, 3349-3378.

[50] Jun Lu and Lu Lin. (2018). Feature screening for multi-response varying coefficient models with ultrahigh dimensional predictors. Computational Statistics and Data Analysis. 128 242–254.

[49] Xiuli Wang, Yunquan Song, Lu Lin. (2017). Handling estimating equation with nonignorably missing data based on SIR algorithm. Journal of Computational and Applied Mathematics. 326, 62-70.

[48]WenWu Wang, Ping Yu, Lu Lin, Tiejun Tong. (2017). Robust Estimation of Derivatives Using Locally Weighted Least Absolute Deviation Regression. Journal of Machine Learning Research 1.1-48.

[47] Lin, L. Liu, Y X, Lin, C. (2017). Mini-max-risk and mini-mean-risk inferences for a partially piecewise regression. Statistics, 51, 745-765.

[46] Qinqin Hu, Lu Lin. (2017). Conditional sure independence screening by conditional marginal empirical likelihood. Annals of the Institute of Statistical Mathematics, 69, 63–96.

[45] Lu Lin, Ping Dong, Yunquan Song and Lixing Zhu. (2017). Upper Expectation Parametric Regression. Statistica Sinica. 27, 1265-1280.

[44] Yunquan Song, Ping Dong, Xiuli Wang and Lu Lin. (2017). Rapid penalized likelihood-based outlier detection via heteroskedasticity test. Journal of Statistical Computation and Simulation. 87:6, 1206-1229.

[43] Wenwu Wang, Lu Lin, Li Yu. (2017). Optimal variance estimation based on lagged second-order difference in nonparametric regression. Comput Stat. 32:1047-1063.

[42] Kangning Wang, Lu Lin. (2017). Robust and efficient direction identification for a groupwise additive multiple-index models and its applications. TEST. 26:22-45.

[41] 盖玉洁, 李锋, 尹钊, 林路, 朱力行（2017）. 自适应的 Dantzig 选择器的渐近性质研究. 中国科学：数学，2017年，第47卷，第7期：869~886.

[40] Lu Lin, Lixing Zhu,Yujie Gai. (2016). Inference for biased models: A quasi-instrumental variable approach. Journal of Multivariate Analysis. 145, 22–36.

[39] Kangning Wang, Lu Lin (2016). Robust structure identification and variable selection in partial linear varying coefficient models. Journal of Statistical Planning and Inference. 174, 153-168.

[37] 林晨，张齐，张晓灵，林路 (2016). 变量约束工具变量回归及其在期权定价和投资组合中的应用. 中国科学: 数学，第46卷，第1 期: 1-16.

[36] 王康宁, 林路. 空间非参回归的变量选择. 中国科学: 数学2016 年第46 卷第3 期: 301-320.

[35] Lu Lin and Jing Sun. (2016). Adaptive conditional feature screening. Computational Statistics and Data Analysis 94, 287–301.

[34] Lu Lin, Yufeng, Shi, Xin Wang and Shuzhen Yang. (2016). k-sample upper expectation linear regression. Journal of Statistical Planning and Inference, 170, 15–26.

[33] Kangning Wang and Lu Lin (2015). Simultaneous structure estimation and variable selection in partial linear varying coefficient models for longitudinal data. Journal of Statistical Computation and Simulation. 85: 1459-1473.

[32] Wenwu Wang and Lu Lin. (2015). Derivative Estimation Based on Difference Sequence via Locally Weighted Least Squares Regresson. Journal of Machine Learning Research. 16, 2617-2641

[31] Xuehu Zhu, Xu Guo, Lu Lin and Lixing Zhu. (2015). Heteroscedasticity Checks for Single Index Models. Journal of Multivariate Analysis. 136, 41–55.

[30] Qinqin Hu, Peng Zeng, Lu Lin. (2015). The dual and degrees of freedom of linearly constrained generalized lasso. Computational Statistics and Data Analysis, 86, 13-26.

[29] Lu Lin, Yunquan Song, Zhao Liu. (2014). Local linear-additive estimation for multiple nonparametric regressions. Journal of Multivariate Analysis. 123, 252–269.

[28] Kangning Wang, Lu Lin. (2014). New efficient estimation and variable selection in models with single-index structure. Statistics and Probability Letters.89, 58-64.

[27] Jing Sun and Lu Lin. (2013). Local rank estimation and related test for varying-coefficient partially linear models. Journal of Nonparametric Statistics. 26, 187-206.

[26] Yujie Gai, Lixing Zhu and Lu Lin. (2013). Model selection consistency of Dantzig selector. Statistica Sinica 23, 615-634.(SCI)

[25] Lu Lin, Jing Sun and Lixing Zhu. (2013). Nonparametric feature screening. Computational Statistics & Data Analysis. 67, 162–174. [24] Xiuli Wang, Fang Chen and Lu Lin. (2013). Empirical likelihood inference for stimating equation with missing data. Science China, Mathematics, 56, 1233-1245.

[23] Jing Su, Yujie Gai, Lu Lin. (2013). Weighted local linear composite quantile estimation for the case of general error distributions. Journal of Statistical Planning and Inference, 143, 1049-1063.

[22] Feng Li, Lu Lin, Yuxia Su. (2013). Variable selection and parameter estimation for partially linear models via Dantzig selctor, Metrika, 76, 225-238.

[21] Gaorong Li, Lu Lin and Lixing Zhu. (2012). Empirical Likelihood for Varying Coefficient Partially Linear Model with Diverging Number of Parameters. Journal of Multivariate Analysis. 105, 85-111.

[20] Lu Lin, Feng Li, Lixing, Zhu (2011). Simulation-based consistent inference for biased working model of non-sparse high-dimensional linear regression. Journal of Statistical Planningand Inference, 141, 3780–3792.

[19] Lu Lin, Qi Zhang, Feng Li, Xia Cui. (2011) .Simulation-based two-stage estimation for multiple nonparametric regression. Computational Statistics & Data Analysis, 55(3), 1367-1378.

[18] Yujie Gaia, Lu Lin, and Xiuli Wang. (2011). Consistent inference for biased sub-model of high-dimensional partially linear model. Journal of Statistical Planning and Inference. 141, 1888-1898.

[17] Xiuli Wang, Gaorong Li and Lu Lin. (2011). Empirical likelihood inference for semi-parametric varying-coefficient partially linear EV models. Metrika, 73, 171-185.

[16] Zhu, L. Lin, L. Cui, X., Li, G. R. (2010). Bias-corrected empirical likelihood in a multi-link semiparametric model. J. Multivariate Anal. 101, 850-868.

[15] Lu Lin, Xia Cui and Lixing Zhu. (2009). An adaptive two-stage estimation method for additive models. Scand. J. Statist. 36, 248-269.

[14] Lixing Zhu, Lu Lin and Qiang Chen. (2009). Adaptive global confidence band for nonparametric regression: an empirical likelihood method. Statistica Sinica, 20, 1771-1787.

[13] Cui，X., Guo, W. S., Lin, L. and Zhu, L. X. (2009). Covariate-adjusted nonlinear regression. Annals of Statistics, 37, 1839-1870.

[12] Lin, L., and Li Feng. (2008). Stable and bias-corrected estimation for nonparametric regression models. Journal of Nonparametric Statistics, 20, 283-303.

[11] Lin, L., Tan, L. (2008). Proper Bayesian estimating equation based on Hilbert Space method. Statistics and Probability Letters. 78,1119-1127.

[10] Lin, L. Fan, Y. Z., Tan, L. (2008) Blockwise bootstrap wavelet in nonparametric regression model with weakly dependent processes. Metrika 67, 31-48.

[9] Yang, G. J., Lin, L. and Zhang, R. C. (2007). Unbiased quasi-regression. China. Ann. Math. 28(B) (2),177-186.

[8] Lin, L. and Cui, X. (2006). Stahel-Donoho kernel estimation for fixed design nonparametric regression models. Science in China Series A: Mathematics, 49(12), 1879-1896.

[7] Lin, L. and Chen, M. H. (2006). Robust Estimating Equation Based on Statistical Depth. Statistical Papers, 47, 263-278.

[6] Lin, L., Zhu, L. X. and Yuen, K. C. (2005) Profile empirical likelihood for parametric and semi-parametric models. Ann. Inst. Statist. Math. 57(3), 485-505.

[5] Lin, L. (2005). Robust depth-weighted wavelet for nonparametric regression models. Acta Mathematica Sinica, English Series, 21, 585 - 592.

[4] Lin, L., Fan, Y. Z., Du, J. and Yuan, Y. (2005). Iterative quasi-likelihood for seemingly unrelated regression systems. Chin. Ann. Math. 26(B) (3),335-346.

[3] Lin, L.(2004). Generalized Quasi likelihood. Statistical Papers, 45,529-544.

[2] Lin, L. (2003). Maximum Information and Optimum Estimating Function. Chin. Ann. Math. 24B, 349-358.

[1] Lin, L. and Zhang, R. C. (2002). Profile Quasi-likelihood. Statistics and Probability Letters. 56, 147-154.

林路 教授

山东大学中泰证券金融研究院教授，博士生导师

[63] Xiaoyu Ma, Lu Lin and Yujie Ga. (2023). A general framework of online updating variable selection for generalized linear models with streaming datasets. Journal of Statistical Computation and Simulation. 93, 325 -- 340.

[62] Qinqin Hu, Lu Lin. (2022). Feature Screening in High Dimensional Regression with Endogenous Covariates. Computational Economics, 60:949 – 969.

[61] 孙晓霏, 王康宁, 李劭珉, 林路. (2022). 基于众数回归的纵向数据部分线性模型的稳健经验似然及变量选择. 中国科学 : 数学, 第 52 卷 第 4 期 : 447 -- 466.

[60] Lu Lin, Lili Liu, Xia Cui and Kangning Wang. (2021). A Generalized Semiparametric Regression and Its Efficient Estimation. Scandinavian journal of statistics. 48,1-24.

[59] Jun Lu, Lu Lin, WenWu Wang. (2021). Partition-based feature screening for categorical data via RKHS Embeddings. Computational Statistics and Data analysis. 157, 107176.

[58] Lili Liu,Mae Gordon, J. Philip Miller, Michael Kass, Lu Lin,Shujie Ma, Lei Liu.(2021). Capturing heterogeneity in repeated measures data by fusion penalty. Statistics in Medicine. 40,1901–1916.

[57] 王康宁, 李劭珉, 林路. (2020). 基于copula 函数的纵向数据复合分位数回归及变量选择. 中国科学: 数学2020 年第50 卷第8 期: 1097-1116.

[56] Jiandong Shi, Dehui Luo, Hong Weng, Xian-Tao Zeng, Lu Lin, Haitao Chu and Tiejun Tong. (2020). Optimally estimating the sample standard deviation from the five‐number summary. Research Synthesis Methods. 11, 642-654.

[55] Yongxin, Liu and Lu Lin. (2019). Classification with minimum ambiguity under distribution heterogeneity. Journal of Statistical Computation and Simulation. 89, 2239–2260.

[54] Wang. W. W., Yu, P., Lin, L. and Tong, T. J. (2019). Robust Estimation of Derivatives Using Locally Weighted Least Absolute Deviation Regression. Journal of Machine Learning Research. 20, 1-49

[53] Lu Lin, Feng Li, Kangning Wang and Lixing Zhu. (2019). Composite estimation: An asymptotically weighted least squares approach. Statistica Sinica 29, 1367-1393.

[52] Hai tao, Liu Lei, Jiang Hongmei and Lin Lu. (2018).A Marginalized Two-Part Beta Regression Model for Microbiome Compositional Data. PLOS Computational Biology. 14(7): e1006329.

[51] Ping Dong, Lu Lin and Yunquan Song. (2018). Significance test of clustering under high dimensional setting with applications to cancer data. Journal of Statistical Computation and Simulation. 88:17, 3349-3378.

[50] Jun Lu and Lu Lin. (2018). Feature screening for multi-response varying coefficient models with ultrahigh dimensional predictors. Computational Statistics and Data Analysis. 128 242–254.

[49] Xiuli Wang, Yunquan Song, Lu Lin. (2017). Handling estimating equation with nonignorably missing data based on SIR algorithm. Journal of Computational and Applied Mathematics. 326, 62-70.

[48]WenWu Wang, Ping Yu, Lu Lin, Tiejun Tong. (2017). Robust Estimation of Derivatives Using Locally Weighted Least Absolute Deviation Regression. Journal of Machine Learning Research 1.1-48.

[47] Lin, L. Liu, Y X, Lin, C. (2017). Mini-max-risk and mini-mean-risk inferences for a partially piecewise regression. Statistics, 51, 745-765.

[46] Qinqin Hu, Lu Lin. (2017). Conditional sure independence screening by conditional marginal empirical likelihood. Annals of the Institute of Statistical Mathematics, 69, 63–96.

[45] Lu Lin, Ping Dong, Yunquan Song and Lixing Zhu. (2017). Upper Expectation Parametric Regression. Statistica Sinica. 27, 1265-1280.

[44] Yunquan Song, Ping Dong, Xiuli Wang and Lu Lin. (2017). Rapid penalized likelihood-based outlier detection via heteroskedasticity test. Journal of Statistical Computation and Simulation. 87:6, 1206-1229.

[43] Wenwu Wang, Lu Lin, Li Yu. (2017). Optimal variance estimation based on lagged second-order difference in nonparametric regression. Comput Stat. 32:1047-1063.

[42] Kangning Wang, Lu Lin. (2017). Robust and efficient direction identification for a groupwise additive multiple-index models and its applications. TEST. 26:22-45.

[41] 盖玉洁, 李锋, 尹钊, 林路, 朱力行（2017）. 自适应的 Dantzig 选择器的渐近性质研究. 中国科学：数学，2017年，第47卷，第7期：869~886.

[40] Lu Lin, Lixing Zhu,Yujie Gai. (2016). Inference for biased models: A quasi-instrumental variable approach. Journal of Multivariate Analysis. 145, 22–36.

[39] Kangning Wang, Lu Lin (2016). Robust structure identification and variable selection in partial linear varying coefficient models. Journal of Statistical Planning and Inference. 174, 153-168.

[37] 林晨，张齐，张晓灵，林路 (2016). 变量约束工具变量回归及其在期权定价和投资组合中的应用. 中国科学: 数学，第46卷，第1 期: 1-16.

[36] 王康宁, 林路. 空间非参回归的变量选择. 中国科学: 数学2016 年第46 卷第3 期: 301-320.

[35] Lu Lin and Jing Sun. (2016). Adaptive conditional feature screening. Computational Statistics and Data Analysis 94, 287–301.

[34] Lu Lin, Yufeng, Shi, Xin Wang and Shuzhen Yang. (2016). k-sample upper expectation linear regression. Journal of Statistical Planning and Inference, 170, 15–26.

[33] Kangning Wang and Lu Lin (2015). Simultaneous structure estimation and variable selection in partial linear varying coefficient models for longitudinal data. Journal of Statistical Computation and Simulation. 85: 1459-1473.

[32] Wenwu Wang and Lu Lin. (2015). Derivative Estimation Based on Difference Sequence via Locally Weighted Least Squares Regresson. Journal of Machine Learning Research. 16, 2617-2641

[31] Xuehu Zhu, Xu Guo, Lu Lin and Lixing Zhu. (2015). Heteroscedasticity Checks for Single Index Models. Journal of Multivariate Analysis. 136, 41–55.

[30] Qinqin Hu, Peng Zeng, Lu Lin. (2015). The dual and degrees of freedom of linearly constrained generalized lasso. Computational Statistics and Data Analysis, 86, 13-26.

[29] Lu Lin, Yunquan Song, Zhao Liu. (2014). Local linear-additive estimation for multiple nonparametric regressions. Journal of Multivariate Analysis. 123, 252–269.

[28] Kangning Wang, Lu Lin. (2014). New efficient estimation and variable selection in models with single-index structure. Statistics and Probability Letters.89, 58-64.

[27] Jing Sun and Lu Lin. (2013). Local rank estimation and related test for varying-coefficient partially linear models. Journal of Nonparametric Statistics. 26, 187-206.

[26] Yujie Gai, Lixing Zhu and Lu Lin. (2013). Model selection consistency of Dantzig selector. Statistica Sinica 23, 615-634.(SCI)

[25] Lu Lin, Jing Sun and Lixing Zhu. (2013). Nonparametric feature screening. Computational Statistics & Data Analysis. 67, 162–174. [24] Xiuli Wang, Fang Chen and Lu Lin. (2013). Empirical likelihood inference for stimating equation with missing data. Science China, Mathematics, 56, 1233-1245.

[23] Jing Su, Yujie Gai, Lu Lin. (2013). Weighted local linear composite quantile estimation for the case of general error distributions. Journal of Statistical Planning and Inference, 143, 1049-1063.

[22] Feng Li, Lu Lin, Yuxia Su. (2013). Variable selection and parameter estimation for partially linear models via Dantzig selctor, Metrika, 76, 225-238.

[21] Gaorong Li, Lu Lin and Lixing Zhu. (2012). Empirical Likelihood for Varying Coefficient Partially Linear Model with Diverging Number of Parameters. Journal of Multivariate Analysis. 105, 85-111.

[20] Lu Lin, Feng Li, Lixing, Zhu (2011). Simulation-based consistent inference for biased working model of non-sparse high-dimensional linear regression. Journal of Statistical Planningand Inference, 141, 3780–3792.

[19] Lu Lin, Qi Zhang, Feng Li, Xia Cui. (2011) .Simulation-based two-stage estimation for multiple nonparametric regression. Computational Statistics & Data Analysis, 55(3), 1367-1378.

[18] Yujie Gaia, Lu Lin, and Xiuli Wang. (2011). Consistent inference for biased sub-model of high-dimensional partially linear model. Journal of Statistical Planning and Inference. 141, 1888-1898.

[17] Xiuli Wang, Gaorong Li and Lu Lin. (2011). Empirical likelihood inference for semi-parametric varying-coefficient partially linear EV models. Metrika, 73, 171-185.

[16] Zhu, L. Lin, L. Cui, X., Li, G. R. (2010). Bias-corrected empirical likelihood in a multi-link semiparametric model. J. Multivariate Anal. 101, 850-868.

[15] Lu Lin, Xia Cui and Lixing Zhu. (2009). An adaptive two-stage estimation method for additive models. Scand. J. Statist. 36, 248-269.

[14] Lixing Zhu, Lu Lin and Qiang Chen. (2009). Adaptive global confidence band for nonparametric regression: an empirical likelihood method. Statistica Sinica, 20, 1771-1787.

[13] Cui，X., Guo, W. S., Lin, L. and Zhu, L. X. (2009). Covariate-adjusted nonlinear regression. Annals of Statistics, 37, 1839-1870.

[12] Lin, L., and Li Feng. (2008). Stable and bias-corrected estimation for nonparametric regression models. Journal of Nonparametric Statistics, 20, 283-303.

[11] Lin, L., Tan, L. (2008). Proper Bayesian estimating equation based on Hilbert Space method. Statistics and Probability Letters. 78,1119-1127.

[10] Lin, L. Fan, Y. Z., Tan, L. (2008) Blockwise bootstrap wavelet in nonparametric regression model with weakly dependent processes. Metrika 67, 31-48.

[9] Yang, G. J., Lin, L. and Zhang, R. C. (2007). Unbiased quasi-regression. China. Ann. Math. 28(B) (2),177-186.

[8] Lin, L. and Cui, X. (2006). Stahel-Donoho kernel estimation for fixed design nonparametric regression models. Science in China Series A: Mathematics, 49(12), 1879-1896.

[7] Lin, L. and Chen, M. H. (2006). Robust Estimating Equation Based on Statistical Depth. Statistical Papers, 47, 263-278.

[6] Lin, L., Zhu, L. X. and Yuen, K. C. (2005) Profile empirical likelihood for parametric and semi-parametric models. Ann. Inst. Statist. Math. 57(3), 485-505.

[5] Lin, L. (2005). Robust depth-weighted wavelet for nonparametric regression models. Acta Mathematica Sinica, English Series, 21, 585 - 592.

[4] Lin, L., Fan, Y. Z., Du, J. and Yuan, Y. (2005). Iterative quasi-likelihood for seemingly unrelated regression systems. Chin. Ann. Math. 26(B) (3),335-346.

[3] Lin, L.(2004). Generalized Quasi likelihood. Statistical Papers, 45,529-544.

[2] Lin, L. (2003). Maximum Information and Optimum Estimating Function. Chin. Ann. Math. 24B, 349-358.

[1] Lin, L. and Zhang, R. C. (2002). Profile Quasi-likelihood. Statistics and Probability Letters. 56, 147-154.