Working paper
Huang, S.-C. and Tsay, R. S. Scalable high-dimensional multivariate linear regression for feature-distributed data. Accepted, Journal of Machine Learning Research. [Preprint] [Codes]
Presentation: International Conference for Statistics and Data Science 2023, Academia Sinica, Taipei (Invited)
Huang, S.-C., Ing, C.-K., and Tsay, R. S. Model selection for unit-root time series with many predictors. Submitted. [Codes]
Presentation: NBER-NSF Time Series 2022, Boston (Plenary session)
Work in progress
1. Wasserstein imputation: Highly versatile missing data imputation for severely damaged time series (with Tengyuan Liang and Ruey S. Tsay)
2. Post empirical Bayes regression (with Sheng-Kai Chang, Yu-Chang Chen, and Shen-Hsun Liao)
3. Time Series forecasting with many predictors (with Ruey S. Tsay)
4. Asymptotic properties of nonstationary ARX models with conditional heteroscedasticity (with Ching-Kang Ing and Ruey S. Tsay)