Long Feng (冯龙)

Education:

• Ph.D., Statistics, Nankai University 2014.

• B.S., Mathematics (Chern Class), Nankai University, 2009.

Working Experience:

• July. 2014--June. 2019, Northeast Normal University, Assistant Professor.

• July. 2019--July 2021, Northeast Normal University, Associate Professor.

• December 2021-now, Nankai University, Associate Professor.

Visiting Experience:                                            

•  March. 2012-June. 2012, Hong Kong Baptist University, Hong Kong. 

•  August. 2013-September. 2013,  National University of Singapore, Singapore.

•  February. 2014-March. 2014,  University of Hong Kong, Hong Kong. 

• January. 2015-January. 2016, University of Florida, USA.

• August. 2021-November. 2021, Southern University of Science and Technology，China.

Research Interest:

• Non-parametric and semi-parametric regression

• High-dimensional data

• Change point detection

Honors and Awards

• Young Changjiang Scholar of the Ministry of Education in 2023

• The "Young Scholar Award" of the 2022 Tianjin Mathematics and Statistics Joint Academic Annual Conference 

• 100 young academic leaders of Nankai University in 2022

• Excellent Doctoral Dissertation of Nankai University in 2015

Projects 

January 2016- -December 2018 

Some studies on ultra high dimension data hypothesis testing problems 

The National Natural Science Foundation Youth Project 

January 2023- -December 2026 

Some studies on high dimensional hypothesis testing problems based on the asymptotic independence between sum and max of random vectors

The National Natural Science Foundation of China

October 2023- -September 2027 

High-dimensional complex data analysis 

Tianjin Science Fund for Outstanding Young Scholar

Publication

1.Feng Long, Zou Changliang and Wang Zhaojun. (2016). Multivariate-sign-based high-dimensional tests for the two-sample location problem, Journal of American Statistical Association. 111, 721-735.

2.Feng Long, Jiang tiefeng, Liu Binghui and Xiong wei. (2022) Max-sum tests for cross-sectional independence of high-dimensional panel data. Annals of Statistics. 50(2), 1124-1143.

3.Wang guanghui and Feng Long*. (2023) Computationally efficient and data-adaptive change point inference in high dimensions. Journal of the Royal Statistical Society: Series B 85(3), 936-958.

4.Zou Changliang, Peng Liuhua, Feng Long and Wang Zhaojun (2014). Multivariate-signs based high-dimensional tests for sphericity. Biometrika. 101(1), 229-236.

5.Zou Changliang, Yin Guosheng, Feng Long and Wang Zhaojun(2014). Nonparametric maximum likelihood approach to multiple change-point problems.  Annals of Statistics. 42 (3), 970-1002.

6.Feng Long, Lan Wei, Liu binghui and Ma yanyuan. (2022) High-dimensional test for alpha in  linear factor pricing models with sparse alternatives. Journal of Econometrics. 229(1), 152-175.

7.Wang hongfei, Liu Binghui, Feng Long* and Ma yanyuan*. (2024). Rank-based max-sum tests for mutual independence of high-dimensional random vectors. Journal of Econometrics 238,105578.

8.Feng Long and Qiu Peihua (2018) Difference detection between two images for image monitoring. Technometrics, 60, 345-359.

9.Feng Long, Liu binghui and Ma yanyuan. (2021) An Inverse Norm Sign Test of Location Parameter for High-Dimensional Data. Journal of Business and Economic Statistics. 39 (3), 807-815.

10.Feng Long, Liu binghui and Ma yanyuan. (2023) A one-sided refined symmetrized data aggregation approach to robust mutual fund selection. Journal of Business and Economic Statistics. Accepted

11.Ma Huifang, Feng Long*, Wang Zhaojun and Bao Jigang (2024) Adaptive Testing for Alphas in Conditional Factor Models with High Dimensional Assets. Journal of Business and Economic Statistics Accepted.

12.Lan Wei, Lei Bo, Feng Long* and Tsai Chih-Ling. (2024). Tests of Equal Predictive Ability Based on Subsampling-Maximum Type Statistic. Journal of Business and Economic Statistics.Accepted.

13.Feng Long, Zou Changliang Wang Zhaojun and Zhu Lixing. (2015) Two Sample Behrens-Fisher problem for high-dimensional data. Statistica Sinica. 25, 1297-1312.

14.Feng Long, Wang Zhaojun, Zhang Chunming and Zou Changliang. (2016) Nonparametric testing in regression models with Wilcoxon-type generalized likelihood ratio. Statistica Sinica. 26, 137-155.

15.Feng Long, Zou Changliang Wang Zhaojun and Zhu Lixing (2017) Composite T-2 test for high dimensional data. Statistica Sinica, 27, 1419-1436.

16.Liu binghui, Feng Long* and Ma yanyuan. (2023) High-dimensional alpha test of linear factor pricing models with heavy-tailed distributions. Statistica Sinica. 33,1389-1410.

17.Feng Long, Jiang Tiefeng, Li Xiaoyun and Liu Binghui. (2023) Asymptotic Independence of the Sum and Maximum of Dependent Random Variables with Applications to High-Dimensional Tests.  Statistica Sinica. Accepted

18.Chen dachuan, Fengyi Song* and Feng Long* (2023) Rank-based tests for high dimensional white noise. Statistica Sinica.Accepted

19.Feng Long, Zou Changliang, and Wang Zhaojun (2012). local walsh average regression. Journal of Multivariate Analysis. 106(1), 36-48.

20.Feng Long, Zou Changliang, and Wang Zhaojun (2012). Rank-based inference for single-index model Statistics and Probability Letters. 82(3), 535-541.

21.Feng Long, Zou Changliang, Wang Zhaojun and Chen bin (2013). Rank-based score tests for high-dimensional regression coefficients. Electronic Journal of Statistics. 7, 2131-2149.

22.Feng Long, Zou Changliang, Wang Zhaojun, Wei Xianwu and Chen bin. (2015). Robust Spline-Based Variable Selection in Varying Coefficient Model.  Metrika. 78 (1), 85-118.

23.Feng Long, Zou Changliang Wang Zhaojun and Zhu Lixing. (2015) Robust comparison of regression curves. Test. 24 (1), 185-204.

24.Feng Longand Sun Fasheng. (2015). A note on the high dimensional two sample test.Statistics and Probability Letters.105, 29-36.

25.Feng Long and Sun Fasheng. (2016). Spatial sign based high dimensional location test. Electronic Journal of Statistics.10, 2420-2434.

26.Feng Long and Liu binhui (2017). High dimensional rank tests for sphericity. Journal of Multivariate Analysis 155, 217-233.

27.Lan wei, Feng Long and Luo ronghua (2018). Testing high dimensional linear asset pricing model. Journal of Financial Econometrics 16 (2), 191-210.

28.Feng Long, Ren haojie and Zou Changliang (2020). A setwise EWMA scheme for monitoring high-dimensional datastreams. Random Matrices: Theory and Applications. 9, 2050004.

29.Feng Long, Zhang xiaoxu and Liu binghui. (2020) A high-dimensional spatial rank test for two-sample location problems. Computational Statistics and Data Analysis. 144,106889.

30.Feng Long, Zhang xiaoxu and Liu binghui. (2020) Multivariate tests of independence and their application in correlation analysis between financial markets. Journal of Multivariate Analysis.179, 104652. 

31.Feng Long, Ding Yanling and Liu Binghui. (2020) Rank-based tests for cross-sectional dependence in large (N,T) fixed effects panel data models. Oxford Bulletin of Economics and Statistics. 82, 1198-1216.

32.Feng Long, Zhao Ping, Ding Yanling, Liu Binghui (2021) Rank-based tests of cross-sectional dependence in panel data models. Computational Statistics and Data Analysis. 153, 107070.

33.Wang hongfei, Feng Long* and Liu Binghui, Zhou Qin.(2021) An inverse norm weight spatial sign test for high-dimensional directional data. Electronic Journal of Statistics, 15(1),3249-3286.

34.Ding Yanling,  Liu Binghui, Zhao Ping  and Feng Long* (2022) Rank-based test for slope homogeneity in high dimensional panel data models. Metrika. 85(5), 605-626.

35.Feng Long, Zhang xiaoxu and Liu binghui (2022) High-dimensional proportionality test of two covariance matrices and its application to gene expression data. Statistical Theory and Related Fields. 6 (2), 161-174.

36.Zhang Xiaoxu, Zhao Ping and Feng Long*. (2022) Robust sphericity test in the panel data model. Statistics and Probability Letters. 182, 109304.

37.Huang Xifen, Liu Binghui, and Zhou Qin and Feng Long*. (2023) A High-dimensional inverse norm sign test for two-sample location problems. Canadian Journal of Statistics.51(4),1004-1033.

38.Meng Jing, Feng Long, Zou Changliang, Wang Zhaojun. (2022) Covariate-Assisted Matrix Completion with Multiple Structural Breaks. Journal of Systems Science & Complexity, Accepted.

39.Wang Guanghui, Feng Long and Zhao Ping. (2023) Tests for slope homogeneity in large panel data model. Communications in Mathematics and Statistics. Accepted.

40.Chen dachuan, Feng Long and Liang Decai. (2023). Asymptotic Independence of the Quadratic form and Maximum of Independent Random Variables with Applications to High-Dimensional Tests. Acta Mathematica, English Series, Accepted.

41.Zhang yuhao, Liu yanhong, Feng Long* and Wang Zhaojun. (2023). Testing The Differential Network Between Two Gaussian Graphical Models With False Discovery Rate Control. Journal Of Statistical Computation And Simulation Accepted.

Revised Papers

42.Lan Wei, Feng Long*, Li Runze and Tsai Chih-Lin. (2023) Testing Multivariate K-Sample Distributions. 

43.Li Zhonghua, Luo Lan, Wang jingshen* and Feng Long* (2023) Efficient quantile covariate adjusted response adaptive experiments. Journal of Econometrics.2nd review

44.Chen Dachuan, Feng Long*, Mykland Per A. and Zhang Lan.(2023)  High Dimensional regression coefficient test with high frequency data. Journal of Econometrics.2nd review

45.Chen Dachuan, Chen Haoning, Feng Long and Xie siyu (2023) High Frequency ANOVA that is Robust to Jumps, Microstructure Noise and Asynchronous Observation Times. Journal of Econometrics.1st revised.

46.Zhang Dongxue, Feng Long, Wu Yujia, Lan Wei and Zhou Jing. Temporal Network Influence Model with Application to the COVID-19 Population Flow Network.Annals of Applied Statistics.2nd revised.

47.Feng Long, Liu Binghui and Ma yanyuan. (2023) Testing for high-dimensional white noise. Statistica Sinica.1st revised.

48.Wang hongfei, Liu Binghui, Feng Long* and Ma yanyuan. (2023) Fisher’s combined probability test for cross-sectional independence in panel data model with serial correlation. Statistica Sinica. 1st revised.

Submitted Papers

49.Chen Dachuan, Feng Long* (2023) Change Point Detection in Beta Process with High Frequency Data. 

50.Wu Yujia,  Lan Wei*,  Feng Long*, and Tsai Chih-Ling (2023) Testing Stochastic Block Models Based on the Maximum Sampling Entry-Wise Deviation. 

51.Ma huifang, Feng Long*, Wang Zhaojun and Bao Jigang (2024) Testing Alpha In Linear Factor Pricing Models With Dependent Observations. 

52.Wang hongfei, Liu Binghui and Feng Long* (2024) Testing Independence Between High-Dimensional Random Vectors Using Rank-Based Max-Sum Tests.

53.Feng Long, Zhang Jun, Lan Wei and Tsai Chih-Ling (2023) Fisher Combination Test of Alpha in Linear Factor Pricing Models with High Dimensional Assets. 

54.Yan Guowei and Feng Long* (2024) Limit Law for the Maximum Interpoint Distance of High Dimensional Dependent Variables. 

55.Zhang Yu and Feng Long* (2024) Adaptive rank-based tests for high dimensional mean problems.

56.Liu Jixuan, Feng Long* and Wang Zhaojun (2024) Spatial-Sign based Maxsum Test for High Dimensional Location Parameters.