Preprints

1. T. Crivelli, B. Cernuschi-Frias, P. Bouthemy and J.-F. Yao.   Mixed-state models in image motion analysis: theory and applications.     Submitted
    
2. Z.D. Bai, J. Chen and J.-F. Yao, 2008.   On estimation of the population spectral distribution from a high-dimensional sample covariance matrix.     Submitted
    
3. Z. D. Bai and J.-F. Yao, 2008.   Central limit theorems for eigenvalues in a generalized spiked population model.     Submitted
    
4. Z. D. Bai, D. Jiang, J. Yao and S. Zheng, 2009. Large Regression Analysis.     Submitted
    
5. J. Chen, B. Delyon and J.-F. Yao, 2009. On a model selection problem from high-dimensional sample covariance matrices.     Submitted
    
6. T. Crivelli, B. Cernuschi-Frias, P. Bouthemy and J.-F. Yao, 2009. Learning mixed-state Markov models for statistical motion texture tracking.     Submitted
    
7. T. Crivelli, B. Cernuschi-Frias, P. Bouthemy, Gwenaelle Piriou, and J.-F. Yao, 2009. Unified mixed-state modeling of decision-estimation problems and its application to motion detection and background reconstruction.     Submitted
    

Recent journal papers

1. M. Kachour and J.-F. Yao, 2009. First-order rounded integer-valued autoregressive (RINAR(1)) process. J. Time Series Analysis 30 (4), 417-448
2. Z. D. Bai, D. Jiang, J. Yao and S. Zheng, 2009.   Corrections to LRT on Large Dimensional Covariance Matrix by RMT. Forthcoming in Annals of Statistics.
3. C. Hardouin and J.-F. Yao, 2008.   Spatial modelling for mixed-state observations     Electronic J. Statistics 2 , 213-233
    
4. C. Hardouin and J.-F. Yao, 2008.   Multi-parameter auto-models and their applications.     Biometrika 95 , 335-349
    
5. Z. D. Bai and J.-F. Yao, 2008.   Central limit theorems for eigenvalues in a spiked population model.     Annales Inst. Henri Poincaré 44 (3), 447-474
    
6. D. Dehay  and J.-F. Yao, 2007.   On likelihhod estimation for  a discretely observed Markov jump process.   Australian New-Zeland J. Statist. 49(1), 93-107
    
7. G. Piriou, P. Bouthemy and J.-F. Yao, 2006.   Recognition of dynamic video contents with global probabilistic motion models.    IEEE Transactions on Image Processing 15(11), 3418-3431
    
8. P. Bouthemy, C. Hardouin, G. Piriou and  J.-F. Yao, 2006.   Mixed-state auto-models and motion texture modeling.    J. Math. Imaging Vision,   25, 387-402
    
9. B. Delyon and J. -F. Yao, 2006.   On the spectral distribution of Gaussian random matrices.   Acta Mathematicae Applicatae Sinica (English Series), 22 (2) :297-312
    
10. Z. D. Bai   and J.-F. Yao, 2005.  On the convergence of the spectral empirical process of Wigner matrices.   Bernoulli,11(6):1059-1092
     
11. B. De Saporta and J.-F. Yao, 2005.  Tail of a linear diffusion with Markov switching    Annals of Applied Probability , 15(1B) , 992-1018
     
12. X. Guyon, S. Iovleff and J.-F. Yao, 2004.  Linear diffusion with stationary switching regime.     ESAIM: Probability and Statistics,   8,  25-35
     
13. C. Gaetan and J.-F. Yao, 2003.  A multiple imputation Metropolis version of the EM algorithm.       Biometrika, 90(3):643-654      
     
14. Z. D. Bai, B.-Q. Miao  and  J.-F. Yao, 2003.  Convergence rates of spectral distributions of large sample covariance matrices.       SIAM  J. Matrix Analysis, 25(1):105-127       Here is a revised copy.
     

Refereed papers in international conferences

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

 
2. T. Crivelli, B. Cernuschi-Frias, P. Bouthemy, J.-F. Yao. Mixed-state Markov Random Fields for Motion Texture Modeling and Segmentation. In Proc. IEEE Int. Conf. on Image Processing (ICIP'06), Atlanta, USA, Octobre 2006.
    
3. A. Lehmann, P Bouthemy, J.-F. Yao. Rank-test similarity measure between video segments for local descriptors. In 4th Int. Workshop on Adaptive Multimedia Retrieval, AMR'2006, Genève, Juillet 2006.
    
4. G. Piriou, P. Bouthemy, J-F. Yao. Motion content recognition in video database with mixed-state probabilistic causal models. In Int. Workshop on Content-Based Multimedia Indexing, CBMI'2005, Riga, Juin 2005.