1 citations · 2 across the 11 of their papers we have counts for
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stat.ME2023
Uncertainty Quantification for Data-Driven Change-Point Learning via Cross-Validation
Hui Chen, Yinxu Jia, Guanghui Wang +1
Accurately detecting multiple change-points is critical for various applications, but determining the optimal number of change-points remains a challenge. Existing approaches based…
stat.ME2023★ 1 cited
Reliever: Relieving the Burden of Costly Model Fits for Changepoint Detection
Chengde Qian, Guanghui Wang, Changliang Zou
Changepoint detection typically relies on a grid-search strategy for optimal data segmentation. When model fitting itself is expensive, repeatedly fitting a model on every candidat…
stat.ME2023★ 1 cited
Zipper: Addressing degeneracy in algorithm-agnostic inference
Geng Chen, Yinxu Jia, Guanghui Wang +1
The widespread use of black box prediction methods has sparked an increasing interest in algorithm/model-agnostic approaches for quantifying goodness-of-fit, with direct ties to sp…