Nonparametric prediction distribution from resolution-wise regression with heterogeneous data

Jialu Li, Wan Zhang, Peiyao Wang, Qizhai Li, Kai Zhang & Yufeng Liu
Modeling and inference for heterogeneous data have gained great interest recently due to rapid developments in personalized marketing. Most existing regression approaches are based on the conditional mean and may require additional cluster information to accommodate data heterogeneity. In this paper, we propose a novel nonparametric resolution-wise regression procedure to provide an estimated distribution of the response instead of one single value. We achieve this by decomposing the information of the response and the predictors...
1 citation reported since publication in 2022.
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