• Corpus ID: 222141799

# 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}
}
• Published 5 October 2020
• Mathematics
• arXiv: Methodology
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…
1 Citations

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