Hung Hung

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20172025

most citedSufficient Dimension Reduction via Random-Partitions for Large-p-Small-n Problem

1 citations · 1 across the 5 of their papers we have counts for

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stat.ME2025

The $ϕ$ -PCA Framework: A Unified and Efficiency-Preserving Approach with Robust Variants

Hung Hung, Zhi-Yu Jou, Su-Yun Huang +1

Principal component analysis (PCA) is a fundamental tool in multivariate statistics, yet its sensitivity to outliers and limitations in distributed environments restrict its effect…

stat.ME2017★ 1 cited

Sufficient Dimension Reduction via Random-Partitions for Large-p-Small-n Problem

Hung Hung, Su-Yun Huang

Sufficient dimension reduction (SDR) is continuing an active research field nowadays for high dimensional data. It aims to estimate the central subspace (CS) without making distrib…

The ϕ-PCA Framework: A Unified and Efficiency-Preserving Approach with Robust Variants

Sufficient Dimension Reduction via Random-Partitions for Large-p-Small-n Problem

The $ϕ$ -PCA Framework: A Unified and Efficiency-Preserving Approach with Robust Variants