Jianfeng YAO

Professor

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

- Weiming Li and Jianfeng Yao, 2017.
On structure testing for component covariance matrices of a
high-dimensional mixture.
*Code and data sets used in the paper:***--> here.** - Keren Shen, Jianfeng Yao and Wai Keung Li, 2017. On a spiked model for large volatility matrix estimation from noisy high-frequency data.
- Keren Shen, Jianfeng Yao and Wai Keung Li, 2016. Forecasting high-dimensional realized volatility matrices using a factor model.
- Zhaoyuan Li and Jianfeng Yao, 2016. Homoscedasticity tests valid in both low and high-dimensional regression.

- Keren Shen, Jianfeng Yao and Wai Keung Li, 2017+. On the Surprising Explanatory Power of Higher Realized Moments in Practice
- Weiming Li, Jiqai Chen and Jianfeng Yao, 2017.
Testing the independence of two random vectors where only one dimension is large.
*Statistics.* - Qinwen Wang and Jianfeng Yao, 2017.
Extreme eigenvalues of large-dimensional spiked Fisher matrices with application.
*The Annals of Statistics.* - Zeng Li, Qinwen Wang and Jianfeg Yao, 2017.
Identifying the number of factors from singular values of a large sample auto-covariance matrix.
*The Annals of Statistics.* - Damien Passemier, Zhaoyuan Li and Jianfeng Yao, 2017.
On estimation of the noise variance in high-dimensional
probabilistic principal component analysis.
*J. Royal Statist. Soc. Series B.**Code and data sets used in the paper:***--> here.** - Shurong Zheng, Zhidong Bai and Jianfeng Yao, 2017.
CLT for linear spectral statistics of large
dimensional general Fisher matrices and its
applications in high-dimensional data analysis.
*Bernoulli*. - Qinwen Wang and Jianfeng Yao, 2016.
Moment approach for singular values distribution of a
large auto-covariance matrix.
*Annals de l'Institut Henri Poincaré. (Probabilités et Statistiques)*. - Zeng Li and Jianfeng Yao, 2016.
Testing the sphericity of a covariance matrix when the dimension is much larger than the sample size.
*Electronic Journal of Statistics.* - 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 - 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 - 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 - Zeng Li, Guangming Pan and Jianfeng Yao, 2015.
On singular value distribution of large-dimensional
autocovariance matrices.
*J. Multivariate Analysis.***137**(May), 119-140 - 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. -
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 - 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. - 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 - 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 -
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 -
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 - 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 - 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 -
Qinwen Wang and Jianfeng Yao, 2013.
On the sphericity test with large-dimensional
observations.
*Electronic J. Statistics***7**,2164-2192. -
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. - 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 -
Z. D. Bai, D. Jiang, J. Yao and S. Zheng, 2013.
Testing linear hypotheses in high-dimensional regressions.
*Statistics***47**(6), 1207-1223 - 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 - Zhidong Bai and Jianfeng Yao, 2012.
On sample eigenvalues in a generalized
spiked population model.
*J. Multivariate Analysis***106**, 167–177. - Jianfeng Yao, 2012.
A note on a Marcenko-Pastur type theorem
for time series.
*Statist. and Probab. Letters***82**, 20-28. - Lionel Truquet and Jianfeng Yao, 2012.
On the quasi-likelihood
estimation for random coefficient autoregressions.
*Statistics***46**(4), 505-521. -
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 - 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 - 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 - 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

- 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
- 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. - 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. - 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. - 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. - 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.