Jianfeng   YAO

Associate Professor

Statistics

Preprints

  1. Zhaoyuan Li and Jianfeng Yao, 2014.   New procedure for high-dimensional classification of general populations     Submitted.
  2.  
  3. S. Zheng, Z. D. Bai and J. Yao, 2014.   Substitution principle for CLT of linear spectral statistics of high-dimensional sample covariance matrices with applications to hypothesis testing     Submitted.
  4.  
  5. S. Zheng, Z. D. Bai and J. Yao, 2013.   CLT for linear spectral statistics of large dimensional general Fisher matrices     Submitted.
  6.  
  7. S. Zheng, Z. D. Bai and J. Yao, 2013.   CLT for linear spectral statistics of random matrix $S^{-1}T$     Submitted.
  8.  
  9. D. Passemier and J. Yao, 2013.   Variance estimation and goodness-of-fit test in a high-dimensional strict factor model     Submitted.
  10.  
  11. Q. Wang, J. Silverstein and J. Yao, 2013.   A note on the CLT of the LSS for sample covariance matrix from a spiked population model     Submitted.
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Recent journal papers (since 2008)

  1. W.M. Li and J. Yao, 2014.   On generalized expectation based estimation of a population spectral distribution from high-dimensional data     Accepted in:   Annals of the Institute of Statistical Mathematics.
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  3. C. Wang, H. Liu, J. Yao, R. Davis and W. K. Li, 2014.   Self-excited Threshold Poisson Autoregression     Accepted in:   J. Amer. Statist. Assoc.
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  5. W.M. Li and J. Yao, 2013.   A local moments estimation of the spectrum of a large dimensional covariance matrix.     Accepted in: Statistica Sinica,     doi:10.5705/ss.2012.130
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  7. N. Raillard, P. Ailliot and J. 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
  8.  
  9. D. Passemier and J. Yao, 2014.   On the detection of the number of spikes, possibly equal, in the high-dimensional case.     J. Multivariate Analysis 127, 173-183
  10.  
  11. Q. Wang and J. Yao, 2013.   On the sphericity test with large-dimensional observations.     Electronic J. Statistics , 7,2164-2192.
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  13. 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.
  14.  
  15. 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
  16.  
  17. Z. D. Bai, D. Jiang, J. Yao and S. Zheng, 2013.  Testing linear hypotheses in high-dimensional regressions.     Statistics, 47(6), 1207-1223    On-line First (2012)    
  18.  
  19. D. Passemier and J-F. Yao, 2012.   On determining the number of spikes in a high-dimensional spiked population model.     Random Matrix: Theory and Applciations 1, 1150002
  20.  
  21. Z. D. Bai and J. Yao, 2012.   On sample eigenvalues in a generalized spiked population model.     J. Multivariate Analysis, 106, 167–177.
  22.  
  23. J-F. Yao, 2012.   A note on a Marcenko-Pastur type theorem for time series.     Statist. and Probab. Letters, 82, 20-28.
  24.  
  25. L. Truquet and J. Yao, 2012.   On the quasi-likelihood estimation for random coefficient autoregressions.     Statistics, 46(4), 505-521.
  26.  
  27. J. Chen, B. Delyon and J. Yao, 2011.   On a model selection problem from high-dimensional sample covariance matrices.     J. Multivariate Anal. 102, 1388–1398
  28.  
  29. 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
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  31. 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
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  33. Z.D. Bai, J. Chen and J. 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
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  35. M. Kachour and J. Yao, 2009. First-order rounded integer-valued autoregressive (RINAR(1)) process. J. Time Series Analysis 30 (4), 417-448
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  37. 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
  38.  
  39. C. Hardouin and J. Yao, 2008.   Spatial modelling for mixed-state observations     Electronic J. Statistics 2 , 213-233
  40.  
  41. C. Hardouin and J. Yao, 2008.   Multi-parameter auto-models and their applications.     Biometrika 95 , 335-349
  42.  
  43. Z. D. Bai and J. Yao, 2008.   Central limit theorems for eigenvalues in a spiked population model.     Annales Inst. Henri Poincaré 44 (3), 447-474
  44.  

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
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  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.
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