Publications in Scientific Journals:

A. Jung:
"Learning the Conditional Independence Structure of Stationary Time Series: A Multitask Learning Approach";
IEEE Transactions on Signal Processing, 63 (2015), 21; 5677 - 5690.

English abstract:
We propose a method for inferring the conditional independence graph (CIG) of a high-dimensional Gaussian vector time series (discrete-time process) from a finite-length observation. By contrast to existing approaches, we do not rely on a parametric process model (such as, e.g., an autoregressive model) for the observed random process. Instead, we only require certain smoothness properties (in the Fourier domain) of the process. 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. A theoretical performance analysis provides sufficient conditions on the sample size such that the new method is consistent asymptotically. Some numerical experiments validate our theoretical performance analysis and demonstrate superior performance of our scheme compared to an existing (parametric) approach in case of model mismatch.

graphical model selection, high-dimensional statistics, multitask LASSO, multitask learning, nonparametric time series, sparsity

"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)

Created from the Publication Database of the Vienna University of Technology.