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Yoon, G.* ; Müller, C. ; Gaynanova, I.*

Fast computation of latent correlations.

J. Comput. Graph. Stat., DOI: 10.1080/10618600.2021.1882468 (2021)
DOI Verlagsversion bestellen
Latent Gaussian copula models provide a powerful means to perform multi-view data integration since these models can seamlessly express dependencies between mixed variable types (binary, continuous, zero-inflated) via latent Gaussian correlations. The estimation of these latent correlations, however, comes at considerable computational cost, having prevented the routine use of these models on high-dimensional data. Here, we propose a new computational approach for estimating latent correlations via a hybrid multilinear interpolation and optimization scheme. Our approach speeds up the current state of the art computation by several orders of magnitude, thus allowing fast computation of latent Gaussian copula models even when the number of variables p is large. We provide theoretical guarantees for the approximation error of our numerical scheme and support its excellent performance on simulated and real-world data. We illustrate the practical advantages of our method on high-dimensional sparse quantitative and relative abundance microbiome data as well as multi-view data from The Cancer Genome Atlas Project. Our method is implemented in the R package mixedCCA, available at https://github.com/irinagain/mixedCCA.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Letter to the Editor
Schlagwörter Bridge Function ; Kendall’s Tau ; Latent Gaussian Copula ; Multilinear Interpolation
ISSN (print) / ISBN 1061-8600
e-ISSN 1537-2715
Verlag Taylor & Francis
Verlagsort 732 N Washington St, Alexandria, Va 22314-1943 Usa
Begutachtungsstatus Peer reviewed
Förderungen Flatiron Institute of the Simons Foundation
National Science Foundation
National Institutes of Health National Cancer Institute