反饋

陳松蹊

陳松蹊，男，1961年11月出生於北京市，數學家^[17] ，統計學家，中國科學院院士，北京大學教授、講席教授。^[12]

1983年，陳松蹊畢業於北京師範大學，獲數學學士學位；1988年，畢業於北京師範大學，獲數學碩士學位；1990年，畢業於惠靈頓維多利亞大學，獲統計與運籌學碩士學位；1992年—1995年，任澳大利亞聯邦科學院（CSIRO）海洋實驗室統計師；1993年，畢業於澳大利亞國立大學，獲統計學博士學位；^[4] 1995年—2000年，任拉籌伯大學（La Trobe University）講師、高級講師（終身教職）；2000年—2003年，任新加坡國立大學副教授；2003年—2017年，任愛荷華州立大學（Iowa State University）統計系終身副教授、教授；2008年，任北京大學教授、講席教授；^[1] 2008年—2013年，任北京大學商務統計與經濟計量繫系主任；^[4] 2010年，創立北京大學統計科學中心，任北京大學統計科學中心首屆聯席主任；^[6] 2014年—2021年，任北京大學商務統計與經濟計量系聯合系主任；^[4] 2021年8月1日，入選2021年中國科學院院士增選初步候選人名單；^[2] 11月18日，當選中國科學院院士。^[3] ^[5] 2023年1月17日，當選第十四屆全國政協委員。^[18]

陳松蹊主要研究方向為超高維大數據統計分析、環境統計、非參數統計方法等。^[6]

中文名: 陳松蹊
國籍: 中國
出生地: 北京市

出生日期: 1961年11月
畢業院校: 澳大利亞國立大學
職業: 教育科研工作者
主要成就: 2021年當選中國科學院院士

陳松蹊人物經歷

陳松蹊(2張)

1961年11月，陳松蹊出生於北京市。^[6] ^[12]

1983年，畢業於北京師範大學，獲數學學士學位。

1988年，畢業於北京師範大學，獲數學碩士學位。

1990年，畢業於惠靈頓維多利亞大學，獲統計與運籌學碩士學位。

1992年—1995年，任澳大利亞聯邦科學院（CSIRO）海洋實驗室統計師。

1993年，畢業於澳大利亞國立大學，獲統計學博士學位。^[4]

1995年—2000年，任拉籌伯大學（La Trobe University）講師、高級講師（終身教職）。

2000年—2003年，任新加坡國立大學副教授。

2003年—2017年，任愛荷華州立大學（Iowa State University）統計系終身副教授、教授。

2008年，任北京大學教授、講席教授。^[1]

2008年—2013年，任北京大學商務統計與經濟計量繫系主任。^[4]

陳松蹊(2張)

2010年，創立北京大學統計科學中心，任北京大學統計科學中心首屆聯席主任。^[6]

2014年—2021年，任北京大學商務統計與經濟計量繫系聯合系主任。^[4]

2021年8月1日，入選2021年中國科學院院士增選初步候選人名單；^[2] 11月18日，當選中國科學院院士。^[3] ^[5]

陳松蹊主要成就

陳松蹊科研成就

科研綜述

陳松蹊以國家大氣污染防治的重大需求為出發點，在數學地球物理領域做出了前沿交叉成果，為精準度量污染排放和評估大氣治理效果提供了科學方法。^[6]

陳松蹊與合作者提出了基於U-統計量和L2範數的超高維均值向量、協方差矩陣和迴歸係數的假設檢驗方法，突破了已有檢驗均要求數據維數和樣本量是同階的限制，在超高維下實現了對假設檢驗第一類錯誤概率的控制。在幾個框架下建立了經驗似然的一階Wilks定理和二階巴特萊特調整，為經驗似然成為基本的非參數統計方法做出了貢獻。^[13]

科研獲獎

獲獎時間	獲獎項目名稱	獎項
2017年	高維數據統計推斷方法	教育部高校科學技術獎自然科學一等獎^[15]

科研項目

項目名稱	批准號	項目類型	時間
高維數據統計建模與分析	1131002	國家自然科學基金重點項目	2012年—2016年
金融連續時間隨機過程的統計推斷	71371016	國家自然科學基金面上項目	2014年—2017年
大數據驅動的管理決策模型與算法	71532001	國家自然科學基金重點項目	2016年—2020年
空氣質量統計診斷模型	2016YFC0207700	國家重點研發專項項目	2016年—2020年
面向兒童腦發育障礙性疾病的神經機制建模與輔助診療算法	12026607	數學與醫療健康交叉重點專項	2021年—2022年
面向管理決策大數據分析的理論與方法	92046021	國家自然科學基金重點項目	2021年—2022年
變係數流行病學模型的統計推斷	12071013	國家自然科學基金面上項目	2021年—2024年
參考資料：^[1]

學術論文

據2024年1月北京大學光華管理學院網站顯示，陳松蹊在學術雜誌發表論文126篇。Web of Science H-指數 31，I-10指數56，總他引3127次。^[4] ^[19]

[1] Gu, J. and Chen, S.X. (2024) Distributed Statistical Inference under Heterogeneity, Journal of Machine Learning Research to appear .

[2] Zheng，Xiangyu and Chen，S.X. （2023）Segmented Linear Regression Trees，Acta Mathematica Sinica，to appear.

[3] Chen, Hanyue, Chen, S.X. and Mu Mu (2023). A Statistical Review on the Optimal Fingerprinting Approach in Climate Change Studies, Climate Dynamics, to appear.

[4] Tong, P.F., Chen, S.X. and Tang, C.Y. (2023) Multivariate calibrations with auxiliary information, Statistica Sinica, to appear.DOI:10.5705/ss.202023.0151

[5] Zheng, Xiangyu and Chen, S.X.(2023) Dynamic synthetic control method for evaluating treatment effects in auto-regressive processes.Journal of the Royal Statistical Society Series B: Statistical Methodology,00:1–22.

[6] Chen, S.X., Qiu, Y.M. and S.Y. Zhang (2023) Sharp Optimality for High Dimensional Covariance Testing under Sparse Signals, The Annals of Statistics,51(5):1921-1945.

[7] Tong, P.F., Zhan, H.X. and Chen, S.X. (2023) Ensembled Seizure Detection based on Small Training Samples, IEEE Transaction on Signal Processing,72: 1-14. DOI：10.1109/TSP.2023.3333546

[8] Zhang, SY, S.X. Chen and Yumou Qiu (2023) Mean Tests For High-dimensional Time Series, Statistica Sinica, to appear.

[9] Peifeng Tong, Wu Su, He Li, Jialin Ding, Haoxiang Zhan, Song Xi Chen (2023). Distribution Free Domain Generalization, Proceedings of the 40th International Conference on Machine Learning(ICML).

[10] Ying Zhang, Song Xi Chen, Le Bao(2023). Air pollution estimation under air stagnation—A case study of Beijing,Environmetrics,34(6), e2819

[11] Zhu Y, Gu J, Qiu Y, Chen SX. （2023）Real-World COVID-19 Vaccine Protection Rates against Infection in the Delta and Omicron Eras. Research，6，Article 0099.

[12] Zhu，YR, Gu, J., Yumou Qiu, S.X. Chen (2023) Estimating COVID-19 Vaccine Protection Rates via Dynamic Epidemiological Models--A Study of Ten Countries, The Annals of Applied Statistics,17(4):3324–3348.

[13] Chen，SX, Guo, B. and Qiu, YM (2023) Testing and Signal Identification for Two-sample High-dimensional Covariances via Multi-level Thresholding, Journal of Econometrics, 235, Issue 2, 1337-1354.

[14] Tong, P. F., Chen, S. X., & Tang, C.Y. (2022). Detecting and evaluating dust-events in North China with ground air quality data. Earth and Space Science, 9, e2021EA001849

[15] Luo, S., Zhu, Y., & Chen, S. X. (2022). Episode based air quality assessment. Atmospheric Environment, 285, 119242.

[16] 陳松蹊，毛曉軍，王聰（2022）大數據情境下的數據完備化：挑戰與對策。管理世界，2022年第1期，196-206.

[17] Li, S-M, Liu, R., Wang, S. and S.X. Chen (2021). Radiative Effects of Particular Matters on Ozone Pollution in Six North China Cities, Journal of Geophysical Research, Vol.126, No. 24, e2021JD035963。

[18] Huang, YX., B. Guo, H. Sun, H. Liu and S. X. Chen(2021) Relative Importance of Meteorological Variables on Air Quality and Role of Boundary Layer Height, Atmospheric Environment,267,118737.

[19] 王振中, 陳松蹊, 塗雲東 (2021)，中國居民消費價格指數的動態結構研究及中美量化比較, 數理統計與管理，12（01）：18。

[20] 顧嘉 , 陳松蹊, 董倩, 邱宇謀（2021）基於vSEIdRm模型的人口遷移以及武漢封城對新冠肺炎疫情發展的影響分析，統計研究，Vol.38, No.9。

[21] Yan, H., Zhu, YR., Gu, J., Huang, YX., Sun, HX., Zhang, XY., Wang, YT., Qiu, YM. and Chen, S.X. (2021). Better strategies for containing COVID-19 pandemic: a study of 25 countries via a vSIADR model, Proceedings of the Royal Society A, 476: 20200440.

[22] Zhu, Y.R.，Liang, Y.S. and Chen, S.X. (2021) Assessing Local Emission for Air Pollution via Data Experiments, Atmospheric Environment, 252, 118323.

[23] [104] Chen, S.X. and L-H Peng (2021) Distributive statistical inference for massive data, The Annals of Statistics, 49, 2851–2869.

[24] Chang, J-Y., Chen, S.X., Tang, C-Y. and Wu, T-T (2021) High-dimensional empirical likelihood inference, Biometrika, 108, 127-147.

[25] Zhang, HM and Chen, S. X. (2021), Concentration Inequalities for Statistical Inference (Review Paper), Communications in Mathematical Research, 37, 1-85. doi: 10.4208/cmr.2020-0041

[26] Chen, S.X. and Zheng, XY (2021) Discussion of ``The timing and effectiveness of implementing mild interventions of COVID-19 in large industrial regions via a synthetic control method", Statistics and Its Interface, 14, 23-24

[27] Zheng, X-Y., Guo, B., He, J. and Chen, S.X. (2021) Effects of COVID-19 Control Measures on Air Quality in North China (Invited Paper), Envirionmentrics, Volume 32, Issue 2，e2673.

[28] 吳煌堅，林偉，孔磊，唐曉，王威，王自發，陳松蹊 (2021) 一種基於集合最優插值的排放源快速反演方法, 《氣候與環境研究》, 第26卷第2期。

[29]Zhang, S., Chen, S.X. and Lu, L. (2021), Inference for Variance Risk Premium, China Finance Review International, 11, 26-52.

[30] Mao, X-J., Wong, R. K-W and Chen, S. X. (2021) Matrix Completion under Low-Rank Missing Mechanism, Statistica Sinica, 31, 2005-2030.

[31]Wu, H., Zheng, X., Zhu, J., Lin, W., Zheng, H., Chen, X., Wang, W., Wang, Z., and S. X. Chen (2020). Improving PM2.5 forecasts in China suing an initial error transport model, Environmental Science and Technology, 54(17), 10493-10501.

[32]Wan, Y., Xu, M., Huang, H. and Chen, S.X. (2020) A spatio-temporal model for the analysis and prediction of fine particulate matter concentration in Beijing, Enviromentrics, 32 (1), e2648.

[33]Haoxuan Sun, Yumou Qiu, Han Yan, Yaxuan Huang, Yuru Zhu, Jia Gu and Song Xi Chen(2020) Tracking Reproductivity of COVID-19 Epidemic in China with Varying Coefficient SIR Model (with discussion)，Journal of Data Science 18 (3), 455–472.

[34]Ziping Xu, Song Xi Chen, Xiaoqing Wu (2020) Meteorological Change and Impacts on Air Pollution Results from North China, Journal of Geophysics Research-Atmosphere, 125 (16), e2020JD032423.

[35] Shuyi Zhang, Song Xi Chen, Bin Guo, Hengfang Wang, Wei Lin (2020) Regional Air-Quality Assessment That Adjusts for Meteorological Confounding, Science China Mathematics, 50, 527-558.

[36]Gu, J., Yan, H., Huang, J., Zhu, Y., Sun, H., Qiu, Y. and S. X. Chen（2020）, Comparing Containment Measures among Nations by Epidemiological Effects of COVID-19. National Science Review, 7: 1847–1851. doi: 10.1093/nsr/nwaa243.

[37] Zheng, XY and Chen, SX (2019) Partitioning Structure Learning for Segmented Linear Regression Trees, Advances in Neural Information Processing Systems (NeurIPS), 2019.

[38] Mao, X., Chen, SX and Wong, R.(2019) Matrix Completion with Covariate Information, Journal of the American Statistical Association, 2019, VOL. 114, NO. 525, 198–210

[39] Chen, S.X., Li, J. and P.-S. Zhong, (2019) Two-Sample and ANOVA Tests for High Dimensional Means, The Annals of Statistics, 47, 1443-1474.

[40] Li, HB, Wu, JW., Wang, AX, Li, X, Chen, SX, Wang, TQ, Amsalu, E., Gao, Q., Luo, YX, Yang, XH., Wang, W, Guo, J., Guo, YM, Guo, XH. (2018). Effects of ambient carbon monoxide on daily hospitalizations for cardiovascular disease: a time-stratified case-crossover study of 460,938 cases in Beijing, China from 2013 to 2017, ENVIRONMENTAL HEALTH, 17:82.

[41] J. He and S. X. Chen (2018) High-Dimensional Two-Sample Covariance Matrix Testing via Super-diagonals, Statistica Sinica, 28, 2671-2696.

[42] Chen, L., Guo, B., Huang, J, He, J., Wang, H., Shuyi Zhang, and S.X. Chen (2018). Assessing air-quality in Beijing-Tianjing-Hebei region: the method and mixed tales of PM2.5 and O3. Atmospheric Environment, 193, 290-301.

[43] Qiu, Y., Chen, S.X. and Nettleton, D.（2018）Detecting Rare and Faint Signals via Thresholding Maximum Likelihood Estimators, Annals of Statistics, 46, 895-923.

[44] Zhang, SY, Guo, B. Dong, A., He, J., Xu, Z and Chen, SX (2017) Cautionary Tales on Air Quality Improvement in Beijing, Proceedings of the Royal Society A, 473: 20170457.

[45] Zuo, T. and S. X. Chen (2017). Enhancing Estimation for Interest Rate Diffusion Models with Bond Prices. Journal of Business and Economics Statistics, 35:3, 486-498.

[46] Guo, B. and S.X.Chen (2016). Tests for High Dimensional Generalized Linear Models. Journal of the Royal Statistical Society, Series B. to 1079-1102.

[47] Wang, Y., Tu, Y-D and S. X. Chen (2016) Improving inflation prediction with the quantity theory. Economics Letters, 149, 112-115.

[48] Chen, S.X. (2016) 　Peter Hall's Contribution to the Bootstrap, The Annals of Statistics, 44, No. 5, 1821–1836.

[49] Liang, X., Li, S., Zhang, SY, Huang, H. and S.X. Chen (2016). PM2.5 Data Reliability, Consistency and Air Quality Assessment in Five Chinese Cities, Journal of Geophysical Research—Atmosphere, 121(17), 10220–10236.

[50] Peng, LH, S.X. Chen and W, Zhou (2016) More Powerful Tests for Sparse High-Dimensional Covariances Matrices, Journal of Multivariate Analysis, 149, 124-143.

[51] He, J. and S. X. Chen (2016) Testing Super-Diagonal Structure in High Dimensional Covariance Matrices, Journal of Econometrics, 194, 283-297

[52] Chen, S.X., Lei, L.-H. and Tu, Y-D (2016). Functional Coefficient Moving Average Models with applications to forecasting Chinese CPI, Statistica Sinica, 26, 1649-1672.

[53] Liang, X., T, Zuo, B. Guo, S. Li, H. Zhang, S. Zhang, H. Huang and S. X. Chen. (2015). Assessing Beijing's PM2.5 Pollution: Severity, Weather Impact, APEC and Winter Heating, Proceedings of the Royal Society A, 471, 20150257.

[54] Chang, J-Y, Chen, S.X. and X. Chen (2015). High Dimensional Generalized Empirical Likelihood for Moment Restrictions with Dependent Data. Journal of Econometrics, 185, 283-304.

[55] Qiu, Y-M and Chen, S.X. (2015) Band Width Selection for High Dimensional Covariance Matrix Estimation. Journal of the American Statistical Association, 110, 1160-1174.

[56] Chen, S.X. and Z. Xu (2014). On Implied Volatility for Options - Some Reasons to Smile and More to Correct. Journal of Econometrics, 179, 1-15.

[57] Chen, S.X. and Z. Xu (2013). On smoothing estimation for seasonal time series with long cycles, Statistics and Its Interface, 6, 435-447.

[58] Chen, S. X., Peng, L. and C. L. Yu (2013). Parameter Estimation and Model Testing for Markov Processes via Conditional Characteristic Functions, Bernoulli, 19, 228-251.

[59] Chen, S. X. and Van Keilegom, I. (2013). Estimation in semiparametric models with missing data. Annals of the Institute of Statistical Mathematics, 65, 785-805.

[60] Chen, S. X., Tang, C.Y. and J. Qin (2013). Mann-Whitney Test with Adjustments to Pre-treatment Variables for Missing Values and Observational Study, Journal of the Royal Statistical Society, Series B., 75, 81-102.

[61] Zhong, P-S, Chen, S. X. and Xu M. (2013). Tests alternative to higher criticism for high dimensional means under sparsity and column-wise dependence, Annals of Statistics, 41, 2820-2851.

[62] Li, J. and S. X. Chen (2012). Two Sample Tests for High Dimensional Covariance Matrices, The Annals of Statistics, 40, 908-940.

[63] Qiu, Y-M and Chen, S. X. (2012). Test for Bandedness of High Dimensional Covariance Matrices with Bandwidth Estimation, TheAnnals of Statistics, 40, 1285-1314.

[64] Chen, S. X. and C. Y. Tang (2011). Nonparametric Regression with Discrete Covariates and Missing Value. Statistics and Its Interface, 4, 463-474.

[65] J. Chang and S.X. Chen (2011). On the approximate maximum likelihood estimation for diffusion processes. The Annals of Statistics, 39, 2820-2851.

[66] Chen, S. X. and C. Y. Tang (2011). Properties of Census Dual System Population Size Estimators. International Statistical Review, 79, 336-361.

[67] P-S Zhong and S. X. Chen (2011). Tests for High Dimensional Regression Coefficients with Factorial Designs. Journal of the American Statistical Association, 106, 260-274.

[68] Chen, S.X. and J. Gao (2011). Simultaneous Specification Test for the Mean and Variance Structures for Nonlinear Time Series regression. Econometric Theory, 27, 2011, 792–843.

[69] Alzghool, R., Y-X Lin and S. X. Chen (2010). Asymptotic Quasi-likelihood Based on Kernel Smoothing for Multivariate Heteroskedastic Models with Correlation, American Journal Of Mathematical And Management Sciences, 30, 147-177.

[70] Chen, S. X. and P-S Zhong (2010). ANOVA for longitudinal data with missing values. The Annals of Statistics, 38, 3630-3659.

[71] Chen, S.X., Zhang, L-X. and P-S Zhong (2010). Testing high dimensional covariance matrices. Journal of the American Statistical Association, 105, 810-819.

[72] Chen, S. X., Delaigle, A. and Hall, P. (2010). Nonparametric estimation for levy-type processes, Journal of Econometrics, 157, 257-271.

[73] Chen, S. X. and Y. L. Qin (2010). A two sample test for high dimensional data with application to gene-set testing, The Annals of Statistics, 38, 808-835.

[74] Chen, S. X., C. Y. Tang and V. T. Mule Jr. (2010). Local Post-Stratification in Dual System Accuracy and Coverage Evaluation for US Census, Journal of the American Statistical Association, Application & Case Studies, 105, 105-119.

[75] Chan, N-H, Chen, S.X., Peng, L. and C. L. Yu (2009). Empirical Likelihood Methods Based on Characteristic Functions with Applications to L\'evy Processes. Journal of the American Statistical Association, 104, 1621-1630.

[76] Chen, S. X. and I. Van Keilegom (2009). A review on empirical likelihood for regressions (with discussions), Test, 3, 415-447 .

[77] Chen, S. X. and Van Keilegom, I. (2009). Empirical likelihood test for a class of regression models. Bernoulli, 15, 955-976.

[78] C. Y. Tang and S. X. Chen (2009). Parameter estimation and bias correction for diffusion processes. Journal of Econometrics, 149, 65—81.

[79] Chen, S. X., L. Peng and Y-L, Qin (2009). Effects of Data Dimension on Empirical Likelihood, Biometrika, 96, 711–722.

[80] Wang, D. and S.X. Chen (2009). Empirical Likelihood for Estimating Equation with Missing Values. The Annals of Statistics, 37, 490–517.

[81] Wang, D. and Chen, S. X. (2009). Combining quantitative trait loci analyses and microarray data, an empirical likelihood approach. Computational Statistics and Data Analysis, 53, 1661–1673.

[82] Chen, S.X. and Chiumin Wong (2009). Smoothed Block Empirical Likelihood for Quantiles of Weakly Dependent Processes, Statist Sinica, 19, 71-82.

[83] Chen, S. X., Leung, D. Y. H. and J. Qin (2008). Improved Semiparametric Estimation Using Surrogate Data. Journal of the Royal Statistical Society, Series B, 70, 803-823.

[84] Chen, S.X., J. Gao and C. Y. Tang (2008). A Test for Model Specification of Diffusion Processes. The Annals of Statistics, 36, 167-198.

[85] Chen, S.X: (2008). Nonparametric Estimation of Expected Shortfall. Journal of Financial Econometrics, 6, 87-107.

[86] Chen, S. X. and T. Huang (2007). Nonparametric Estimation of Copula Functions for Dependent Modeling. Canadian Journal of Statistics, 35, 265-282.

[87] Chen, S.X. and H.-J., Cui (2007). On the second order properties of empirical likelihood with moment restrictions , Journal of Econometrics, 141, 492-516.

[88] Chen, S.X. and J. Gao (2007). An Adaptive Empirical Likelihood Test For Time Series Models, paper, full report, Journal of Econometrics, 141, 950-972.

[89] Chen, S.X. and H.-J., Cui (2006). On Bartlett Correction of Empirical Likelihood in the Presence of Nuisance Parameters, Biometrika, 93, 215-220.

[90] Chen, S.X. and Qin, J. (2006). An Empirical likelihood Method in Mixture Models with Incomplete Classifications, Statistica Sinica,16, 1101-1115.

[91] Chen, S. X. and Tang, C. Y. (2005). Nonparametric Inference of Value at Risk for dependent Financial Returns. Journal of Financial Econometrics, 3, 227-255.

[92] Chen, S. X. and Qin, Y-S. (2003). Coverage accuracy of confidence intervals in nonparametric regression. Acta Math. Appl. Sin. Engl. Ser.19,387--396.

[93] Chen, S. X., D. H. Y. Leung and Qin, J. (2003). Information Recovery in a Study with Surrogate Endpoints. Journal of the American Statistical Association, 98,1052--1062.

[94] Chen, S. X. and Qin, J. (2003). Empirical likelihood based confidence intervals for data with possible zero observations. Statistics and Probability Letters, 65, 29-37.

[95] Chen, S. X., Haredle, W. and Li, M. (2003). An empirical likelihood goodness-of-fit test for time series. Journal of The Royal Statistical Society, Series B, 65, 663-678.

[96] Chen, S. X. and Hall, P. (2003). Effects of bagging and bias correction on estimators defined by estimating equations, Statistica Sinica,13, 97-109.

[97] Chen, S. X and Cui, H-J. (2003). An extended empirical likelihood for generalized linear models. Statistica Sinica, 13, 69-81.

[98] Chen, S. X. and Hall, P. (2003). EFFECTS OF BAGGING AND BIAS CORRECTION ON ESTIMATORS DEFINED BY ESTIMATING EQUATIONS, Statistica Sinica, 13, 97-109.

[99] Chen, S. X., Hardle, W. and Kleinow, T. (2002). An empirical likelihood goodness-of-fit test for diffusions. Applied quantitative finance, 259--281, Springer, Berlin.

[100] Chen, S. X, Yip, P. and Zhou, Y. (2002). Sequential line transect surveys. Biometrics, 58, 263-269.

[101] Chen, S. X. (2002). Local linear smoothers using asymmetric kernels. Ann. Inst. Statist. Math., 54, 312-323.

[102] Chen, S. X. and Lloyd, C. J.(2002). Estimation of population size based on biased samples using nonparametric binary regression. Statist. Sinica, 12, 505-518.

[103] Chen, S. X. and Qin, Yong Song (2002). Confidence interval based on a local linear smoother. Scand. J. Statist., 29, 89-99.

[104] Chen, S. X. and Cowling, A. (2001). Measurement Errors in Line Transect Surveys where Detection varies with Distance and Size. Biometrics, 57, 732-742.

[105] Chen, S. X. and Qin, Yong Song (2000). Empirical Likelihood confidence interval for a local linear smoother. Biometrika, 87, 946-953.

[106] Chen, S. X. and Lloyd, C. J. (2000). A non-parametric approach to the analysis of two stage mark-recapture experiments.Biometrika, 87, 633-649.

[107] Chen, S. X. (2000). Gamma kernel estimators for density functions. Ann. Inst. Statist. Math. 52, 471-480.

[108] Chen, S. X. (2000). Animal abundance estimation for independent observer line transect surveys. Special Issue of Environmental and Ecological Statistics: Statistical Ecology and Forest Biometry 7, No. 3, 285-299.

[109] Chen, S. X. (2000). Beta kernel smoothers for regression curves. Statistica Sinica.10, 73-91.

[110] Chen, S. X. (1999). Beta kernel estimators for density functions. Computational Statistics and Data Analysis, 31, 131-145.

[111] Chen, S. X. and Woolcock, J. (1999). A condition for designing bus-route type access site surveys to estimate recreational fishing effort. Biometrics. 55, No. 3, 799-804.

[112] Chen, S. X. (1999). Estimation in independent observer line transect surveys for clustered populations. Biometrics, 55 , No. 3, 754-759.

[113] Brown, B. M. and Chen, S. X. (1999). Beta-Bernstein smoothing for regression curves with compact support. Scand. J. Statist. . 26, 47-59.

[114] Brown, B. M. and Chen, S. X. (1998). Combined Empirical Likelihood. Ann. Inst. Statist. Math, 50, 697-714.

[115] Chen, S.X. (1998). Measurement errors in line transect surveys. Biometrics, 54, 899-908.

[116] Chen, S.X. (1997). Empirical likelihood for nonparametric density estimation. Aust. J. Statist. , 39,47-56

[117] Chen, S.X. and Polacheck, T. (1996). Kernel estimates of mean school size for IWC minke whale data. Report of International Whaling Commission, 46, 341-348.

[118] Chen, S.X. (1996). Empirical likelihood confidence intervals for nonparametric density estimation. Biometrika, 83, 329-341.

[119] Chen, S.X. (1996). Studying school size effects in line transect sampling using the kernel method. Biometrics , 52, 1283-94.

[120] Chen, S.X. (1996). A kernel estimate for density of a biological population using line transect sampling. Royal Statistical Society Ser. C: Applied Statistics, 45, 135-150.

[121] Chen, S.X. (1994). Comparing empirical likelihood and bootstrap hypothesis tests. J. Mult. Anal, 51, 277-293.

[122] Chen, S.X. (1994). Empirical likelihood confidence intervals for linear regression coefficients. J. Mult. Anal. 49, 24-40.

[123] Chen, S.X. and Hall, P. (1994). On the calculation of standard error for quotation in confidence statements. Statistics and Probability Letters,19,147-151.

[124] Chen, S.X. and Hall, P. (1993). Smoothed empirical likelihood confidence intervals for quantiles. Ann. Of Statistics, 21,1166-1181.

[125] Chen, S.X. (1993). On the coverage accuracy of empirical likelihood confidence regions for linear regression model. Annals of Institute of Statistical Mathematics, 45, 621-637.

[126] Chen, S.X., Smith, P.J., Shafi, M. and Vere-Jones, D. (1990). Some improvements to conventional importance sampling techniques for coded system using Viterbi decoding. Electronics Letters, 26, 802-806.^[19]

陳松蹊人才培養

主講課程

陳松蹊主講課程：高等多元統計分析、大樣本統計理論。^[4]

培養的研究生

博士研究生：王瑩（中國人民大學經濟學院教師）^[8] 、張澍一^[9]

碩士研究生：孫浩軒^[10]

科研態度

陳松蹊認為做研究一定要保持一個向上的心態，保持積極的心態，要有強烈的內驅力、有耐性。^[11]

寄語學生

陳松蹊

畢業生要確立更高的目標，同時正確看待人生中的曲折和彎路，享受不確定性帶來的可能性和驚喜，在困難面前永不放棄，努力為推動社會進步貢獻北師大人的智慧。^[11]

陳松蹊榮譽表彰

獲獎時間	獎項名稱
1989年	新西蘭電信獎學金（Telecom New Zealand）獎學金^[4]
1990年—1992年	澳大利亞國立大學（Australian National University）博士獎^[4]
2008年	愛荷華州立大學（Iowa State University）教員傑出研究獎^[4]
2009年	美國統計學會會士^[4]
2021年11月18日	中國科學院院士^[12]
	美國科學促進會會士^[7]
	數理統計研究所會士^[7]

陳松蹊社會任職

時間	擔任職務
2008年—2009年	國際華人統計學會理事會成員^[7]
2010年—2013年	《統計及其接口》（《Statistics and Its Interface》）聯席主編^[4]
2010年—2019年	《統計年鑑》（《The Annals of Statistics》）編委^[4]
2013年—2018年	《商業與經濟統計雜誌》（《Journal of Business and Economic Statistics》）編委^[4]
2016年—2019年	國際數理統計學會理事會常務理事^[7]
2017年—	國家統計局諮詢委員^[1]
2018年—2020年	《美國統計學會會刊》（《Journal of the American Statistical Association》）^[4]
2018年—	《環境》副主編^[4]
	中國統計學會常務理事^[7]
	伯努利學會科學書記^[7]
	第十四屆全國政協委員^[16]