- Chang, J., Fang, Q., Qiao, X., & Yao, Q. (2024+). On the modeling and prediction of high-dimensional functional time series. Journal of the American Statistical Association, in press.
- Chang, J., He, J., Yang, L., & Yao, Q. (2023). Modelling matrix time series via a tensor CP-decomposition. Journal of the Royal Statistical Society: Series B, 85, 127-148.
- Chang, J., Guo, B., & Yao, Q. (2018). Principal component analysis for second-order stationary vector time series. The Annals of Statistics, 46, 2094-2124.
- Chang, J., Tang, C. Y., & Wu, Y. (2016). Local independence feature screening for nonparametric and semiparametric models by marginal empirical likelihood. The Annals of Statistics, 44, 515-539.
- Chang, J., Guo, B., & Yao, Q. (2015). High dimensional stochastic regression with latent factors, endogeneity and nonlinearity. Journal of Econometrics, 189, 297-312.
- Chang, J., Tang, C. Y., & Wu, Y. (2013). Marginal empirical likelihood and sure independence feature screening. The Annals of Statistics, 41, 2123-2148.
