Hierarchical variable clustering based on the predictive strength between random vectors
Abstract
A rank-invariant clustering of variables is introduced that is based on the predictive strength between groups of variables, i.e., two groups are assigned a high similarity if the variables in the first group contain high predictive information about the behaviour of the variables in the other group and/or vice versa. The method presented here is model-free, dependence-based and does not require any distributional assumptions. Various general invariance and continuity properties are investigated, with special attention to those that are beneficial for the agglomerative hierarchical clustering procedure. A fully non-parametric estimator is considered whose excellent performance is demonstrated in several simulation studies and by means of real-data examples.
Cite
@article{arxiv.2312.16544,
title = {Hierarchical variable clustering based on the predictive strength between random vectors},
author = {Sebastian Fuchs and Yuping Wang},
journal= {arXiv preprint arXiv:2312.16544},
year = {2023}
}