Jianfeng   YAO

Associate Professor

Preprints

  1. Zeng Li, Qinwen Wang and Jianfeg Yao, 2014.   Identifying the number of factors from singular values of a large sample auto-covariance matrix    
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  3. Qinwen Wang and Jianfeng Yao, 2014.   Moment approach for singular values distribution of a large auto-covariance matrix    
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  5. Damien Passemier, Zhaoyuan Li and Jianfeng Yao, 2014.   On estimation of the noise variance in high-dimensional probabilistic principal component analysis,     Code and data sets used in the paper:     --> here.
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  7. Zeng Li, Guangming Pan and Jianfeng Yao, 2014.   On singular value distribution of large-dimensional autocovariance matrices.    
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  9. Zhaoyuan Li and Jianfeng Yao, 2014.   New procedure for high-dimensional classification of general populations.    
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  11. Shurong Zheng, Zhidong Bai and Jianfeng Yao, 2013.   CLT for linear spectral statistics of large dimensional general Fisher matrices.    
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  13. Shurong Zheng, Zhidong Bai and Jianfeng Yao, 2013.   CLT for linear spectral statistics of random matrix $S^{-1}T$.    
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Recent journal papers (since 2008)

  1. 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.     Ann. Statist. (accepted)
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  3. 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.
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  5. 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
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  7. 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
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  9. Weiming Li and Jianfeng 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.     doi:10.1007/s10463-014-0452-2
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  11. 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
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  13. 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
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  15. 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
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  17. 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
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  19. Qinwen Wang and Jianfeng Yao, 2013.   On the sphericity test with large-dimensional observations.     Electronic J. Statistics 7,2164-2192.
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  21. 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.
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  23. 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
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  25. 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)    
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  27. 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
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  29. Zhidong Bai and Jianfeng Yao, 2012.   On sample eigenvalues in a generalized spiked population model.     J. Multivariate Analysis 106, 167–177.
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  31. Jianfeng Yao, 2012.   A note on a Marcenko-Pastur type theorem for time series.     Statist. and Probab. Letters 82, 20-28.
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  33. Lionel Truquet and Jianfeng Yao, 2012.   On the quasi-likelihood estimation for random coefficient autoregressions.     Statistics 46(4), 505-521.
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  35. 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
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  37. 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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  39. 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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  41. 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
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  43. Maher Kachour and Jianfeng Yao, 2009. First-order rounded integer-valued autoregressive (RINAR(1)) process. J. Time Series Analysis 30 (4), 417-448
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  45. Zhidong Bai, Dandan Jiang, Jianfeng Yao and Shurong Zheng, 2009.   Corrections to LRT on Large Dimensional Covariance Matrix by RMT. Ann. Statistics 37 (6B), 3822–3840
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  47. C. Hardouin and J. Yao, 2008.   Spatial modelling for mixed-state observations     Electronic J. Statistics 2 , 213-233
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  49. C. Hardouin and J. Yao, 2008.   Multi-parameter auto-models and their applications.     Biometrika 95 , 335-349
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  51. Zhidong Bai and Jianfeng Yao, 2008.   Central limit theorems for eigenvalues in a spiked population model.     Annales Inst. Henri Poincaré 44(3), 447-474
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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.
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  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.
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  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.
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  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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  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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