106 citations · 311 across the 18 of their papers we have counts for
19 papers
High-Dimensional Importance-Weighted Information Criteria: Theory and Optimality
Yong-Syun Cao, Shinpei Imori, Ching-Kang Ing
Imori and Ing (2025) proposed the importance-weighted orthogonal greedy algorithm (IWOGA) for model selection in high-dimensional misspecified regression models under covariate shi…
Model Selection for Unit-root Time Series with Many Predictors
Shuo-Chieh Huang, Ching-Kang Ing, Ruey S. Tsay
This paper studies model selection for general unit-root time series, including the case with many exogenous predictors. We propose a new model selection algorithm, FHTD, that leve…
Negative Moment Bounds for Sample Autocovariance Matrices of Stationary Processes Driven by Conditional Heteroscedastic Errors and Their Applications
Hsueh-Han Huang, Ching-Kang Ing, Shu-Hui Yu
We establish a negative moment bound for the sample autocovariance matrix of a stationary process driven by conditional heteroscedastic errors. This moment bound enables us to asym…
High-Dimensional Knockoffs Inference for Time Series Data
Chien-Ming Chi, Yingying Fan, Ching-Kang Ing +1
We make some initial attempt to establish the theoretical and methodological foundation for the model-X knockoffs inference for time series data. We suggest the method of time seri…
Inference of Random Effects for Linear Mixed-Effects Models with a Fixed Number of Clusters
Chih-Hao Chang, Hsin-Cheng Huang, Ching-Kang Ing
We consider a linear mixed-effects model with a clustered structure, where the parameters are estimated using maximum likelihood (ML) based on possibly unbalanced data. Inference w…
A generalized information criterion for high-dimensional PCA rank selection
Hung Hung, Su-Yun Huang, Ching-Kang Ing
Principal component analysis (PCA) is the most commonly used statistical procedure for dimension reduction. An important issue for applying PCA is to determine the rank, which is t…