Uncertainty propagation in shrinkage-based partial correlations
- Autores: Victor Bernal, Victor Guryev, Rainer Bischoff, Péter Horvatovich, Marco Grzegorczyk
- Localización: Proceedings of the 35th International Workshop on Statistical Modelling : July 20-24, 2020 Bilbao, Basque Country, Spain / Itziar Irigoien Garbizu (ed. lit.), Dae-Jin Lee (ed. lit.), Joaquín Martínez Minaya (ed. lit.), María Xosé Rodríguez Álvarez (ed. lit.), 2020, ISBN 978-84-1319-267-3, págs. 294-297
- Idioma: inglés
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Resumen
Gaussian graphical models (GGMs) are network models where random variables are represented by nodes and their pair-wise partial correlation by edges. The inference of a GGM demands the estimation of the precision matrix (i.e. the inverse of the covariance matrix); however, this becomes problematic when the number of variables is larger than the sample size. Covariance estimators based on shrinkage (a type of regularization) overcome these pitfalls and result in a 'shrunk' version of the GGM. Traditionally, shrinkage is justi ed at model level (as a regularized covariance). In this work, we re-interpret the shrinkage from a data level perspective (as a regularized data). Our result allows the propagation of uncertainty from the data into the GGM structure.
