姚建峰

姚建峰（2021年11月加入SDS）

校长讲座教授

巴黎萨克雷大学博士（原巴黎第十一大学）

研究领域：

随机矩阵理论和高维统计、高维计量经济学模型、马尔可夫链和马尔可夫过程、时间序列分析、网络数据分析、数字图像分析

个人网站：

https://jianfengyao.wordpress.com/

个人简介：

姚建峰教授于1990年至2000年在巴黎第一大学索邦大学担任助理教授和副教授。2000年起担任雷恩第一大学数学系应用数学专业正教授。他还于1989-1994年在SUDIMAGE R&D、于2003-2004年在法国INRIA担任访问职位。2011年起在香港大学担任副教授和正教授。姚建峰教授同时也是山东大学数学学院特聘教授。

姚教授是随机矩阵理论和高维统计领域的国际领先学者。他的著作《大样本协方差矩阵和高维数据分析》（剑桥大学出版社，2015，与白志东、郑术蓉合著）是该领域的权威参考书。姚教授因其研究贡献曾多次获奖。姚教授为美国数理统计学会会士、国际统计学会会员、伯努利数理统计与概率学会理事会成员。姚建峰教授担任多家著名期刊现任编委，如Journal of Multivariate Analysis、Random Matrices: Theory and Applications，曾任Bernoulli期刊编委。他曾获得评价：“对随机矩阵理论在高维数据分析中的推论方面做出了有影响力的贡献”。

学术著作：

[A] Book

Jianfeng Yao, Shurong Zheng and Zhidong Bai. Large Sample Covariance Matrices and High-Dimensional Data Analysis (Cambridge University Press, 2015, co-authored with Zhidong Bai and Shurong Zheng)

[B] Journal papers

1.Zeng Li, Fang Han and Jianfeng Yao, 2020. Asymptotic joint distribution of extreme eigenvalues and trace of    large sample covariance matrix in a generalized spiked population model. The Annals of Statistics 48(6) (December), 3138-3160

2.Jian Song, Jianfeng Yao and Wangjun Yuan, 2020. High-dimensional limits of eigenvalue distributions for general Wishart process. The Annals of Applied Probability 30 (4) (August), 1642-1668

3.Z. Li, C. Lam, J. Yao and Q. Yao, 2019. On testing for high-dimensional white noise. The Annals of Statistics, 47(6) (December 2019), 3382-3412

4.Weiming Li and Jianfeng Yao 2018. On structure testing for component covariance matrices of a high-dimensional mixture. Journal of the Royal Statistical Society Series B (Statistical Methodology) 80 (Part 2) (February), 293-318

5.Q. Wang and J. Yao, 2017. Extreme eigenvalues of large-dimensional spiked Fisher matrices with application. The Annals of Statistics 45(1) (February), 415-460.

6.Z. Li, Q. Wang and J. Yao, 2017. Identifying the number of factors from singular values of a large sample auto-covariance matrix. The Annals of Statistics 02/2017; 45(1) (February), 257-288

7.Qinwen Wang and Jianfeng Yao, 2016. Moment approach for singular values distribution of a large auto-covariance matrix. Annals de l’Institut Henri Poincaré – Probabilités et Statistiques 52 (4), 1641-1666

8.D. Passemier, Zh. Li and J. Yao, 2017. On estimation of the noise variance in high-dimensional probabilistic principal component analysis. Journal of the Royal Statistical Society Series B (Statistical Methodology) 79(1) (January), 51-67.

9.S. Zheng, Z. D. Bai and J. Yao, 2017. CLT for eigenvalue statistics of high-dimensional general Fisher matrices with applications. Bernoulli 23(2) (April), 1130-1178.

10.S. Zheng, Z. D. Bai and J. Yao, 2015. Substitution principle for CLT of linear spectral statistics of high-dimensional sample covariance matrices with applications to hypothesis testing. The Annals of Statistics 43 (2), 546-591

11.Q. Wang, Z. Su and J. Yao, 2014. Joint CLT for several random sesquilinear forms with applications to large-dimensional spiked population models. Electron. J. Probab. 19 (103), 1-28.

12.C. Wang, H. Liu, J.F. Yao, R. Davis and W. K. Li, 2014. Self-excited Threshold Poisson Autoregression.  J. Amer. Statist. Assoc. 109 (506, June 2014), 777-787

13.N. Raillard, P. Ailliot and J. Yao, 2014. Modeling extreme values of processes observed at irregular time steps: application to significant wave height. The Annals of Applied Statistics 8 (1) (March), 622–647

14.T. Crivelli, B. Cernuschi-Frias, J.F. Yao and P. Bouthemy, 2013. Motion textures: modeling, classification and segmentation using mixed-state Markov random fields. SIAM J. Imaging Science 6(4), 2484–2520.

15.T. Crivelli, P. Bouthemy, B. Cernuschi-Frı́as and J. Yao, 2011. Simultaneous motion detection and background reconstruction with a conditional mixed-state Markov random field. Int. J. Computer Vision 94, 295–316.

16.Z. D. Bai, D. Jiang, J. Yao and S. Zheng, 2009. Corrections to LRT on Large Dimensional Covariance Matrix by RMT. Ann. Statistics 37 (6B), 3822-3840.

17.Z. D. Bai and J.-F. Yao, 2008. Central limit theorems for eigenvalues in a spiked population model. Annals de l’Institut Henri Poincaré – Probabilités et Statistiques 44 (3), 447-474.

18.C. Hardouin and J.-F. Yao, 2008. Multi-parameter auto-models and their applications. Biometrika 95, 335-349.

19.P. Bouthemy, C. Hardouin, G. Piriou and J.-F. Yao, 2006. Mixed-state auto-models and motion texture modeling. Journal of Mathematical Imaging and Vision, 25, 387-402.

20.Z. D. Bai and J.-F. Yao, 2005. On the convergence of the spectral empirical process of Wigner matrices. Bernoulli, 11(6):1059-1092.

21.B. De Saporta et J.-F. Yao, 2005. Tail of a linear diffusion with Markov switching. Annals of Applied Probability, 15(1B):992-1018.

22.C. Gaetan and J.-F. Yao, 2003. A multiple imputation Metropolis version of the EM algorithm. Biometrika, 90(3):643-654.

23.Z. D. Bai, B.-Q. Miao and J.-F Yao, 2002. Convergence rates of spectral distributions of large sample covariance matrices. SIAM J. Matrix Analysis 25(1):105-127.

24.J.F. Yao and J.G. Attali, 2000. On stability of nonlinear AR processes with Markov switching. Adv. Applied Probab., 32:394-407.

[C] Top conferences in Computer Science

1.T. Crivelli, B. Cernuschi-Frias, P. Bouthemy, J.F. Yao, 2009. Learning mixed-state Markov models for statistical motion texture tracking In Inter. Conf. Computer Vision (ICCV’09), Kyoto, Japan, September 2009.

2.T. Crivelli, G. Piriou, B. Cernuschi-Frias, P. Bouthemy, J.F. Yao, 2008. Simultaneous motion detection and background reconstruction with a mixed-state conditional Markov random field. In Proc. Eur. Conf. Computer Vision (ECCV’08), Volume 1, Pages 113-126, Marseille, France

3.G. Piriou, P. Bouthemy, J-F. Yao, 2004. Extraction of semantic dynamic content from videos with probabilistic motion models. In European conference on computer vision, ECCV’04, Prague

更多已出版著作，请点击【阅读原文】姚教授的Google Scholar网页。