Abstract Gaussian graph model which studies the relationships between the independent random variables is one of the topic of machine learning. In this paper, a method is proposed to link the estimation of separate graphical models through a hierarchical penalty. By studying the highdimensional statistical properties of the new model, the asymptotic consistency and sparsistency of the proposed parameter estimation are proved.
Received: 20 December 2013
Published: 28 July 2018
ZHANG Ling-jie, ZHANG Hai. A group bridge approach for joint estimation of multiple graphical models. Applied Mathematics A Journal of Chinese Universities, 2014, 29(2): 127-137.
