Hierarchical variable clustering based on the predictive strength between random vectors

作者：Sebastian Fuchs, Yuping Wang · 发表于：International Journal of Approximate Reasoning · 年份：2024 · DOI：10.1016/j.ijar.2024.109185 · 被引用次数：11 · 研究领域：Bayesian Methods and Mixture Models、Soil Geostatistics and Mapping、Statistical Methods and Bayesian Inference

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.