Random Forests Weighted Local Fr\'echet Regression with Theoretical Guarantee. (arXiv:2202.04912v1 [stat.ML])

Statistical analysis is increasingly confronted with complex data from
general metric spaces, such as symmetric positive definite matrix-valued data
and probability distribution functions. [47] and [17] establish a general
paradigm of Fr\'echet regression with complex metric space valued responses and
Euclidean predictors. However, their proposed local Fr\'echet regression
approach involves nonparametric kernel smoothing and suffers from the curse of
dimensionality. To address this issue, we in this paper propose a novel random
forests weighted local Fr\'echet regression paradigm. The main …

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