MODEL ASSISTED VARIABLE CLUSTERING: MINIMAX-OPTIMAL RECOVERY AND ALGORITHMS.

ABSTRACT: The problem of variable clustering is that of estimating groups of similar components of a p-dimensional vector X = (X ₁, … , X _p ) from n independent copies of X. There exists a large number of algorithms that return data-dependent groups of variables, but their interpretation is limited to the algorithm that produced them. An alternative is model-based clustering, in which one begins by defining population level clusters relative to a model that embeds notions of similarity. Algorithms tailored to such models yield estimated clusters with a clear statistical interpretation. We take this view here and introduce the class of G-block covariance models as a background model for variable clustering. In such models,

SUBMITTER: Bunea F 

PROVIDER: S-EPMC9286061 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature