Professor Qin's current research effort is mainly devoted to hypothesis testing for high-dimensional data with applications to gene sets testing and estimating and testing for large dimensional covariance matrices using the random matrix theory.
- PhD in Statistics, Iowa State University, U.S.A.
- MA in Statistics, Iowa State University, U.S.A.
- BSc in Applied Mathematics, Northeast Normal University, China
- Li, W. and Qin, Y. (2014). Hypothesis testing for high-dimensional covariance matrices. Journal of Multivariate Analysis. Accepted.
- Xia, N., Qin, Y. and Bai, Z. (2013). Convergence rates of eigenvector empirical spectral distribution of large dimensional sample covariance matrix. The Annals of Statistics. Accepted.
- Li, W., Chen, J., Qin, Y. , Bai, Z. and Yao, J. (2013). Estimation of the population spectral distribution from a large dimensional sample covariance matrix. Journal of Statistical Planning and Inference. Accepted.
- Qin, Y. (2011). Discussion: Two-stage procedures for high-dimensional data. Sequential Analysis, 30, 416-422.
- Chen, S. and Qin, Y. (2010). A two-sample test for high-dimensional data with applications to gene-set testing. The Annals of Statistics, 38 (2), 808-835.
- Chen, S., Peng, L.. and Qin, Y. (2009). Effects of Data Dimension on Empirical Likelihood. Biometrika, 96 (3), 711-722.