High-Dimensional Variable Clustering based on Maxima of a Weakly Dependent Random Process
基于弱依赖随机过程极大值的高维变量聚类
We propose a new class of models for variable clustering called Asymptotic Independent block (AI-block) models, which defines population-level clusters based on the independence of the maxima of a multivariate stationary mixing random process among clusters. This class of models is identifiable, meaning that there exists a maximal element with a partial order between partitions, allowing for statistical inference. We also present an algorithm depending on a tuning parameter…