Description:

Greedy algorithms, particularly the orthogonal greedy algorithm (OGA), are frequently used for high-dimensional model selection as an alternative to Lasso. In this course, I will discuss the statistical properties of OGA when applied in conjunction with high-dimensional criteria for model selection in both stationary and non-stationary high-dimensional time series models. Additionally, I will explain how greedy-type algorithms can be employed to estimate high-dimensional sparse covariance matrices of stationary time series. Furthermore, I will demonstrate modifications of OGA for high-dimensional model selection in the presence of covariate shift. If time permits, I will introduce the performance of the Chebyshev Greedy Algorithm (CGA), a non-linear counterpart of OGA, in certain high-dimensional non-linear models.

Teaching Language: Chinese

Bio:

Ching-Kang Ing is currently a Chair Professor at NTHU, Taiwan. His expertise encompasses mathematical statistics, model selection in time series, and high-dimensional data analysis. He has gained international recognition and has received numerous prestigious awards in Taiwan, including the Outstanding Research Awards from the Ministry of Science and Technology (MOST) in 2008 and 2013, the Academia Sinica Investigator Award in 2011, the Science Vanguard Research Award from MOST in 2016, the Outstanding Scholar Award from the Foundation for the Advancement of Outstanding Scholarship in 2017, and the Sun Yat-Sen Academic Award from the Sun Yat-Sen Academic and Cultural Foundation in 2020.