Resumen de Correction for the shrinkage effect in gaussian graphical models
Victor Guryev, Rainer Bischoff, Péter Horvatovich, Marco Grzegorczyk, Victor Bernal
Gaussian graphical models (GGMs) are probabilistic graphical models based on partial correlation. A GGM consists of a network of nodes (representing the random variables) connected by edges (their partial correlation). To infer a GGM, the inverse of the covariance matrix (the precision matrix) is required. The main challenge is that when the number of variables is larger than the sample size, the (sample) covariance is ill conditioned (or not invertible). Shrinkage methods consist in regularizing the estimator of the covariance matrix to make it invertible (and well conditioned); however, the e ect of the shrinkage on the nal network topology has not been studied so far.
