Identification of Non-causal Graphical Models
This work addresses a specific modeling challenge in statistical learning, but appears incremental as it builds on existing graphical model frameworks.
The paper tackled the problem of estimating non-causal graphical models by proposing a new covariance extension method, resulting in a double-sided autoregressive model that minimizes transportation distance with respect to white noise.
The paper considers the problem to estimate non-causal graphical models whose edges encode smoothing relations among the variables. We propose a new covariance extension problem and show that the solution minimizing the transportation distance with respect to white noise process is a double-sided autoregressive non-causal graphical model. Then, we generalize the paradigm to a class of graphical autoregressive moving-average models. Finally, we test the performance of the proposed method through some numerical experiments.