  We formulate and analyze a graphical model selection method for inferring the
conditional independence graph of a high-dimensional nonstationary Gaussian
random process (time series) from a finite-length observation. The observed
process samples are assumed uncorrelated over time and having a time-varying
marginal distribution. The selection method is based on testing conditional
variances obtained for small subsets of process components. This allows to cope
with the high-dimensional regime, where the sample size can be (drastically)
smaller than the process dimension. We characterize the required sample size
such that the proposed selection method is successful with high probability.
