Research Interests

       Complex correlated systems can be molded by random matrices. Random matrices can be                         characterized by eigenvalues, which have a characteristic pattern even though the components of             various complex correlated systems are very different. This phenomenon is known as universality.

  • High-dimensional statistics

       Mathematical tools from random matrix theory have found important applications in high-                           dimensional statistics, like testing and inference about large covariance matrix, independence                     testing, large-dimensional PCA and factor model, high-dimensional regression analysis, and machine         learning etc..

       If your data set has a large number of variables compared to available sample size, where traditional         multivariate statistical methods fail and dimensional reduction is not applicable. My methods will               work.

 

Selected Journal Papers (corresponding author*)

1. Li Z. Y. and Yao J. F.* (2019). Testing for heteroscedasticity in high-dimensional regressions.                          Econometrics and Statistics, 9: 122-139. [link]

2. Passemier D., Li Z. Y.* and Yao J. F. (2017). On estimation of the noise variance in high-dimensional              probabilistic principal component analysis. Journal of the Royal Statistical Society Series B (Statistical          Methodology), 79: 51-67. [link]

3. Li Z. Y. and Tian M. Z.* (2017). A new method for dynamic clustering based on spectral analysis.                    Computational Economics, 50:373-392. [link]

4. Li Z. Y.* and Yao J. F. (2016). On two simple and effective procedures for high-dimensional classification      of general populations. Statistical Papers, 57: 381-405. [link]

5. Yalamanchili H., Li Z. Y. , Wang P. W., Wong M. Yao J F.* and Wang J. W.* (2014).  SpliceNet: recovering      splicing isoform specific differential gene networks from RNA-Seq data of normal and diseased                  samples. Nucleic Acids Research, 42: e121. [link]

6. Li Z. Y., Liu S. B. and Tian M. Z.* (2014). Momentum effect differs across stock performances: Chinese          evidence. Acta Mathematicae Applicatae Sinica, English Series, 30: 279-288. [link]