Random Forest Weighted Local Fr\'echet Regression with Random Objects

arXiv:2202.04912v4 Announce Type: replace-cross
Abstract: Statistical analysis is increasingly confronted with complex data from metric spaces. Petersen and M\"uller (2019) established a general paradigm of Fr\'echet regression with complex metric space valued responses and Euclidean predictors. However, the local approach therein involves nonparametric kernel smoothing and suffers from the curse of dimensionality. To address this issue, we in this paper propose a novel random forest weighted local Fr\'echet regression paradigm. The main mechanism of our approach relies on a locally …

abstract analysis arxiv cs.lg data general however kernel objects paradigm random regression responses space spaces statistical stat.ml type