## References

SHOWING 1-10 OF 67 REFERENCES

### Graphical Modeling Of High-Dimensional Time Series

- Computer Science, Mathematics2018 52nd Asilomar Conference on Signals, Systems, and Computers
- 2018

A novel formulation of joint graphical lasso in frequency domain, suitable for dependent time series, generalizing current time-domain approaches to i.i.d. time series is presented.

### Consistency of Sparse-Group Lasso Graphical Model Selection for Time Series

- Computer Science, Mathematics2020 54th Asilomar Conference on Signals, Systems, and Computers
- 2020

Enough conditions are established for consistency of the inverse PSD estimator resulting from the sparse-group graphical lasso-based approach to inferring the conditional independence graph of a high-dimensional stationary multivariate Gaussian time series.

### Learning the Conditional Independence Structure of Stationary Time Series: A Multitask Learning Approach

- Computer ScienceIEEE Transactions on Signal Processing
- 2015

The proposed inference scheme works even for sample sizes much smaller than the number of scalar process components if the true underlying CIG is sufficiently sparse and a theoretical performance analysis provides sufficient conditions on the sample size such that the new method is consistent asymptotically.

### Compressive nonparametric graphical model selection for time series

- Computer Science, Mathematics2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- 2014

This work provides analytical conditions for the method to correctly identify the CIG of a high-dimensional discrete-time Gaussian vector random process from finite-length observations and assumes certain spectral smoothness properties.

### On Sparse Complex Gaussian Graphical Model Selection

- Computer Science, Mathematics2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP)
- 2019

The focus of this paper is on theoretical analysis of a recently proposed graphical lasso approach based on an $\ell_{1} -$penalized log-likelihood objective function to estimate the sparse inverse covariance matrix.

### Sparse permutation invariant covariance estimation

- Computer Science, Mathematics
- 2008

A method for constructing a sparse estimator for the inverse covariance (concentration) matrix in high-dimensional settings using a penalized normal likelihood approach and forces sparsity by using a lasso-type penalty is proposed.

### Bayesian Structure Learning for Stationary Time Series

- Computer Science, MathematicsUAI
- 2015

This work takes a Bayesian approach to structure learning, placing priors on (i) the graph structure and (ii) spectral matrices given the graph, and uses a Whittle likelihood approximation and a conjugate prior to define a hyper complex inverse Wishart on the complex-valued and graph-constrained spectralMatrices.

### Learning graphical models for stationary time series

- Computer ScienceIEEE Transactions on Signal Processing
- 2004

This paper describes an algorithm for efficient forecasting for stationary Gaussian time series whose spectral densities factorize in a graphical model and shows how to make use of Mercer kernels in this setting, allowing the ideas to be extended to nonlinear models.

### An Efficient Approach to Graphical Modeling of Time Series

- Computer ScienceIEEE Transactions on Signal Processing
- 2015

It is shown that reformulation in terms of a multiple hypothesis test reduces computation time by O(p2) and simulations support the assertion that power levels are attained at least as good as those achieved by Matsuda's much slower approach.

### On the Sample Complexity of Graphical Model Selection From Non-Stationary Samples

- Computer Science, MathematicsIEEE Transactions on Signal Processing
- 2020

A sufficient condition on the required sample size for accurate graphical model selection based on non-stationary data is derived by analyzing a conceptually simple model selection method.