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Identification and Estimation of Graphical Continuous Lyapunov Models

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If you have a question about this talk, please contact Qingyuan Zhao .

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Graphical continuous Lyapunov models offer a new perspective on modeling causally interpretable dependence structure in multivariate data by treating each independent observation as a one-time cross-sectional snapshot of a temporal process. Specifically, the models consider multivariate Ornstein-Uhlenbeck processes in equilibrium. This leads to Gaussian models in which the covariance matrix is determined by the continuous Lyapunov equation. In this setting, each graphical model assumes a sparse drift matrix with support defined by a directed graph. The talk will discuss the identifiability of such sparse drift matrices and their regularized estimation.

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