林路教授-中泰证券金融研究院

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林路教授

山东大学中泰证券金融研究院教授，博士生导师

重要兼职：

第一届和第二届教育部应用统计专业硕士教育指导委员会成员

山东省教育厅专业学位研究生教育指导委员会成员

山东省政府参事

奖励：

获得国家统计局颁发的全国统计科学研究优秀成果一等奖和二等奖(排名第一)

山东省优秀教学成果一等奖(排名第一)

主要教育和工作经历：

南开大学博士，南开大学副教授，山东大学教授

联系方式:

办公电话：0531-88364791

E-mail：linlu@sdu.edu.cn

林路教授

山东大学中泰证券金融研究院教授，博士生导师

在国内外统计学、机器学习和数学等顶级期刊(包括Annals of Statistics, Journal of Machine Learning Research, 中国科学)和其它重要期刊发表研究论文120余篇，多个资政报告得到主管省长的重要批示。

主要论文：

[66] Yan Chen, Shuixin Fang and Lu Lin. Renewable composite quantile method and algorithm for nonparametric models with streaming data. Statistics and Computing, (2024) 34:43.

[65] Lili Liu, Kevin He, Di Wang, Shujie Ma, Annie Qu, Lu Lin, J. Philip Miller, Lei Liu. Healthcare center clustering for Cox's proportional hazards model by fusion penalty. Statistics in Medicine. 2023; 42:3685–3698.

[64] Lin Lu and Li Feng. (2023). Global debiased DC estimations for biased estimators via pro forma regression. TEST, 32:726–758.

[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.