17 citations · 32 across the 15 of their papers we have counts for
9 papers · 1 filter
On the efficiency-loss free ordering-robustness of product-PCA
Hung Hung, Su-Yun Huang
This article studies the robustness of the eigenvalue ordering, an important issue when estimating the leading eigen-subspace by principal component analysis (PCA). In Yata and Aos…
Robust self-tuning semiparametric PCA for contaminated elliptical distribution
Hung Hung, Su-Yun Huang, Shinto Eguchi
Principal component analysis (PCA) is one of the most popular dimension reduction methods. The usual PCA is known to be sensitive to the presence of outliers, and thus many robust…
Statistical Inference on the Cure Time
Yueh Wang, Hung Hung
In population-based cancer survival analysis, the net survival is important for government to assess health care programs. For decades, it is observed that the net survival reaches…
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…
A robust RUV-testing procedure via gamma-divergence
Hung Hung
Identification of differentially expressed genes (DE-genes) is commonly conducted in modern biomedical researches. However, unwanted variation inevitably arises during the data col…
Sufficient dimension reduction with additional information
Hung Hung, Chih-Yen Liu, Henry Horng-Shing Lu
Sufficient dimension reduction is widely applied to help model building between the response and covariate . While the target of interest is the relationship between …