8 citations · 10 across the 4 of their papers we have counts for
4 papers
Analyze Additive and Interaction Effects via Collaborative Trees
Chien-Ming Chi
We present Collaborative Trees, a novel tree model designed for regression prediction, along with its bagging version, which aims to analyze complex statistical associations betwee…
FACT: High-Dimensional Random Forests Inference
Chien-Ming Chi, Yingying Fan, Jinchi Lv
Quantifying the usefulness of individual features in random forests learning can greatly enhance its interpretability. Existing studies have shown that some popularly used feature…
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…
Asymptotic Properties of High-Dimensional Random Forests
Chien-Ming Chi, Patrick Vossler, Yingying Fan +1
As a flexible nonparametric learning tool, the random forests algorithm has been widely applied to various real applications with appealing empirical performance, even in the prese…