no code implementations • 6 May 2019 • Shu Wang, Jonathan G. Yabes, Chung-Chou H. Chang
However, algorithms that can cluster data with mixed variable types (continuous and categorical) remain limited, despite the abundance of data with mixed types particularly in the medical field.
no code implementations • 9 May 2019 • Shu Wang, Jonathan G. Yabes, Chung-Chou H. Chang
To address these challenges, we propose a Bayesian finite mixture model to simultaneously conduct variable selection, account for biomarker LOD and obtain clustering results.
1 code implementation • 10 Mar 2021 • Xiaoqing Tan, Chung-Chou H. Chang, Ling Zhou, Lu Tang
We propose a tree-based model averaging approach to improve the estimation accuracy of conditional average treatment effects (CATE) at a target site by leveraging models derived from other potentially heterogeneous sites, without them sharing subject-level data.