Jianfeng   YAO

Associate Professor

Statistics

Preprints

  1. Z. Li, Q. Wang and J. Yao, 2014.   Identifying the number of factors from singular values of a large sample auto-covariance matrix     Submitted.
  2.  
  3. Q. Wang and J. Yao, 2014.   Moment approach for singular values distribution of a large auto-covariance matrix     Submitted.
  4.  
  5. D. Passemier, Zh. Li and J. Yao, 2014.   On estimation of the noise variance in high-dimensional probabilistic principal component analysis,     Submitted.
  6.  
  7. Q. Wang and J. Yao, 2014.   Joint CLT for several random sesquilinear forms with applications to large-dimensional spiked population models.     Submitted.
  8.  
  9. Z. Li, G. Pan and J. Yao, 2014.   On singular value distribution of large-dimensional autocovariance matrices.     Submitted.
  10.  
  11. Zh. Li and J. Yao, 2014.   New procedure for high-dimensional classification of general populations.     Submitted.
  12.  
  13. 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.
  14.  
  15. S. Zheng, Z. D. Bai and J. Yao, 2013.   CLT for linear spectral statistics of large dimensional general Fisher matrices.     Submitted.
  16.  
  17. S. Zheng, Z. D. Bai and J. Yao, 2013.   CLT for linear spectral statistics of random matrix $S^{-1}T$.     Submitted.
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Recent journal papers (since 2008)

  1. 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
  2.  
  3. Q. Wang, J. Silverstein and J. 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
  4.  
  5. W.M. Li and J. Yao, 2014.   On generalized expectation based estimation of a population spectral distribution from high-dimensional data     In Press:   Annals of the Institute of Statistical Mathematics.
  6.  
  7. C. Wang, H. Liu, J. Yao, R. Davis and W. K. Li, 2014.   Self-excited Threshold Poisson Autoregression     J. Amer. Statist. Assoc. 109 (506, June 2014), 777-787
  8.  
  9. W.M. Li and J. Yao, 2014.   A local moments estimation of the spectrum of a large dimensional covariance matrix.     Statistica Sinica 24 (2, April), 919-936
  10.  
  11. 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
  12.  
  13. 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
  14.  
  15. Q. Wang and J. Yao, 2013.   On the sphericity test with large-dimensional observations.     Electronic J. Statistics 7,2164-2192.
  16.  
  17. 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.
  18.  
  19. 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
  20.  
  21. 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)    
  22.  
  23. 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
  24.  
  25. Z. D. Bai and J. Yao, 2012.   On sample eigenvalues in a generalized spiked population model.     J. Multivariate Analysis 106, 167–177.
  26.  
  27. J-F. Yao, 2012.   A note on a Marcenko-Pastur type theorem for time series.     Statist. and Probab. Letters 82, 20-28.
  28.  
  29. L. Truquet and J. Yao, 2012.   On the quasi-likelihood estimation for random coefficient autoregressions.     Statistics 46(4), 505-521.
  30.  
  31. J. Chen, B. Delyon and J. Yao, 2011.   On a model selection problem from high-dimensional sample covariance matrices.     J. Multivariate Analysis 102, 1388–1398
  32.  
  33. 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
  34.  
  35. 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
  36.  
  37. 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
  38.  
  39. M. Kachour and J. Yao, 2009. First-order rounded integer-valued autoregressive (RINAR(1)) process. J. Time Series Analysis 30 (4), 417-448
  40.  
  41. 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
  42.  
  43. C. Hardouin and J. Yao, 2008.   Spatial modelling for mixed-state observations     Electronic J. Statistics 2 , 213-233
  44.  
  45. C. Hardouin and J. Yao, 2008.   Multi-parameter auto-models and their applications.     Biometrika 95 , 335-349
  46.  
  47. 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
  48.  

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.
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