Jianfeng   YAO

Professor

Book

                       book cover published by Cambridge University Press (March 2015).        Read some draft chapters    here.

Preprints

  1. Keren Shen, Jianfeng Yao and Wai Keung Li, 2015.  Forecasting high-dimensional realized volatility matrices using a factor model.    
  2.  
  3. Zeng Li and Jianfeng Yao, 2015.   Testing the sphericity of a covariance matrix when the dimension is much larger than the sample size.    
  4.  
  5. Zhaoyuan Li and Jianfeng Yao, 2015.   Homoscedasticity tests valid in both low and high-dimensional regression.    
  6.  
  7. Weiming Li and Jianfeng Yao, 2015.   Testing the independence of two random vectors where only one dimension is large.    
  8.  

Journal papers (since 2010)

  1. Qinwen Wang and Jianfeng Yao, 2016+.  Extreme eigenvalues of large-dimensional spiked Fisher matrices with application.     Forthcoming in: The Annals of Statistics
  2.  
  3. Zeng Li, Qinwen Wang and Jianfeg Yao, 2016+.   Identifying the number of factors from singular values of a large sample auto-covariance matrix.     Forthcoming in: The Annals of Statistics
  4.  
  5. Damien Passemier, Zhaoyuan Li and Jianfeng Yao, 2015+.   On estimation of the noise variance in high-dimensional probabilistic principal component analysis.     Forthcoming in: J. Royal Statist. Soc. Series B.     Code and data sets used in the paper:     --> here.
  6.  
  7. Shurong Zheng, Zhidong Bai and Jianfeng Yao, 2015+.   CLT for linear spectral statistics of large dimensional general Fisher matrices and its applications in high-dimensional data analysis.     Forthcoming in: Bernoulli    
  8.  
  9. Qinwen Wang and Jianfeng Yao, 2015+.   Moment approach for singular values distribution of a large auto-covariance matrix.     Forthcoming in: Annals de l'Institut Henri Poincaré. (Probabilités et Statistiques)
  10.  
  11. Zhaoyuan Li and Jianfeng Yao, 2016.   On two simple and effective procedures for high dimensional classification of general populations.     Statistical Papers 57 (2), 381-405
  12.  
  13. Qinwen Wang and Jianfeng Yao, 2015.   On singular values distribution of a large auto-covariance matrix in the ultra-dimensional regime.     Random Matrices: Theory and Applications 4 (4), 1550015 (October 2015).   DOI: 10.1142/S201032631550015X
  14.  
  15. Shurong Zheng, Zhidong Bai and Jianfeng Yao, 2015.   CLT for linear spectral statistics of a rescaled sample precision matrix.     Random Matrices: Theory and Applications 4 (4), 1550014 (October 2015).   DOI: 10.1142/S2010326315500148
  16.  
  17. Zeng Li, Guangming Pan and Jianfeng Yao, 2015.   On singular value distribution of large-dimensional autocovariance matrices.     J. Multivariate Analysis. 137 (May), 119-140
  18.  
  19. Shurong Zheng, Zhidong Bai and Jianfeng 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) (April), 546–591.
  20.  
  21. Weiming Li and Jianfeng Yao, 2015.   On generalized expectation based estimation of a population spectral distribution from high-dimensional data     Annals of the Institute of Statistical Mathematics 67 (2) (April 2015), 359-373    
  22.  
  23. Qinwen Wang, Zhonggen Su and Jianfeng Yao, 2014.   Joint CLT for several random sesquilinear forms with applications to large-dimensional spiked population models.     Electron. J. Probab. 19, no. 103, 1-28.
  24.  
  25. H. K. Yalamanchili, Zh. Li, P. Wang, M. P. Wong, J. Yao and J. Wang, 2014.   SpliceNet: recovering splicing isoform-specific differential gene networks from RNA-Seq data of normal and diseased samples.     Nucleic Acids Research.   DOI: 10.1093/nar/gku577
  26.  
  27. Qinwen Wang, Jack Silverstein and Jianfeng Yao, 2014.   A note on the CLT of the LSS for sample covariance matrix from a spiked population model     J. Multivariate Analysis 130, 194-207
  28.  
  29. Chao Wang, Heng Liu, Jianfeng Yao, Richard A. Davis and Wai Keung Li, 2014.   Self-excited Threshold Poisson Autoregression     J. Amer. Statist. Assoc. 109 (506, June 2014), 777-787
  30.  
  31. Weiming Li and Jianfeng Yao, 2014.   A local moments estimation of the spectrum of a large dimensional covariance matrix.     Statistica Sinica 24 (2, April), 919-936
  32.  
  33. Nicolas Raillard, Pierre Ailliot and Jianfeng Yao, 2014.   Modeling extreme values of processes observed at irregular time steps: application to significant wave height     Annals of Applied Statistics 8 (1), 622–647
  34.  
  35. Damien Passemier and Jianfeng Yao, 2014.   On the detection of the number of spikes, possibly equal, in the high-dimensional case.     J. Multivariate Analysis 127, 173-183
  36.  
  37. Qinwen Wang and Jianfeng Yao, 2013.   On the sphericity test with large-dimensional observations.     Electronic J. Statistics 7,2164-2192.
  38.  
  39. T. Crivelli, B. Cernuschi-Frias, P. Bouthemy and J. Yao, 2013.   Motion textures: modeling, classification and segmentation using mixed-state Markov random fields.     SIAM Journal on Imaging Sciences 6(4), 2484–2520.
  40.  
  41. W.M. Li, J.Q. Chen, Y.L. Qin, J. Yao and Z.D. Bai, 2013.   Estimation of the population spectral distribution from a large dimensional sample covariance matrix.   J. Statistical Planning and Inference 143 (11, November), 1887–1897
  42.  
  43. Z. D. Bai, D. Jiang, J. Yao and S. Zheng, 2013.  Testing linear hypotheses in high-dimensional regressions.     Statistics 47(6), 1207-1223   
  44.  
  45. Damien Passemier and Jianfeng Yao, 2012.   On determining the number of spikes in a high-dimensional spiked population model.     Random Matrix: Theory and Applciations 1, 1150002
  46.  
  47. Zhidong Bai and Jianfeng Yao, 2012.   On sample eigenvalues in a generalized spiked population model.     J. Multivariate Analysis 106, 167–177.
  48.  
  49. Jianfeng Yao, 2012.   A note on a Marcenko-Pastur type theorem for time series.     Statist. and Probab. Letters 82, 20-28.
  50.  
  51. Lionel Truquet and Jianfeng Yao, 2012.   On the quasi-likelihood estimation for random coefficient autoregressions.     Statistics 46(4), 505-521.
  52.  
  53. Jiaqi Chen, Bernard Delyon and Jianfeng Yao, 2011.   On a model selection problem from high-dimensional sample covariance matrices.     J. Multivariate Analysis 102, 1388–1398
  54.  
  55. T. Crivelli, · P. Bouthemy, · B. Cernuschi-Frias 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
  56.  
  57. T. Crivelli, · P. Bouthemy, · B. Cernuschi-Frias and J. Yao, 2010.   Mixed-state causal modeling for statistical KL-based motion texture tracking.     Pattern Recognition Letters 31 (14), 2286-2294
  58.  
  59. Zhidong Bai, Jiaqi Chen and Jianfeng Yao, 2010.   On estimation of the population spectral distribution from a high-dimensional sample covariance matrix.     Australian & New Zeland Journal of Statistics 52, 423-437
  60.  

Refereed papers in int. conferences (since 2008)

  1. B. Belmudez, V. Prinet, J. Yao, P. Bouthemy and X. Descombes, 2009. Conditional mixed-state model for structural change analysis from very high resolution optical images. International Geoscience and Remote Sensing Symposium (IGARSS) Volume 2, 2009, Pages II988-II991
  2.  
  3. Th. Crivelli, P. Bouthemy, B. Cernuschi-Frias, and J. Yao, 2009. Learning mixed-state Markov models for statistical motion texture tracking. In Proc. ICCV'09, Int. Workshop on Machine Learning for Vision-based Motion Analysis (MLVMA'09), Kyoto, Japan, October 2009.
  4.  
  5. Th. Crivelli, G. Piriou, B. Cernuschi-Frias, P. Bouthemy, J. 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, October 2008.
  6.  
  7. Th. Crivelli, B. Cernuschi-Frias, P. Bouthemy, J. Yao, 2008. Temporal modeling of motion textures with mixed-states Markov chains. In Proc. Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP'08), Pages 881-884, Las Vegas, USA, April 2008.
  8.  
  9. Th. Crivelli, B. Cernuschi-Frias, P. Bouthemy, J. Yao, 2008.. Recognition of dynamic video contents based on motion texture statistical models. In Proc. Int. Conf. on Computer Vision Theory and Applications (VISAPP'08), Volume 1, Pages 283-289, Funchal, Portugal, January 2008.
  10.  
  11. J. Yao and D. Passemier, 2014 On estimation of the noise variance in a high-dimensional signal detection model. In Proc. 2014 IEEE Workshop on Statistical Signal Processing (SSP) , Pages 17-20, Gold Coast, Australia, July 2014.
  12.