# Detecting approximate replicate components of a high-dimensional random vector with latent structure

@article{Bing2020DetectingAR, title={Detecting approximate replicate components of a high-dimensional random vector with latent structure}, author={Xin Bing and Florentina Bunea and Marten H. Wegkamp}, journal={arXiv: Methodology}, year={2020} }

High-dimensional feature vectors are likely to contain sets of measurements that are approximate replicates of one another. In complex applications, or automated data collection, these feature sets are not known a priori, and need to be determined. This work proposes a class of latent factor models on the observed high-dimensional random vector $X \in \mathbb{R}^p$, for defining, identifying and estimating the index set of its approximately replicate components. The model class is parametrized…

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