Learning conditional independence structure for high-dimensional uncorrelated vector processes

Nguyen Tran Quang*, Alexander Jung

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

5 Citations (Scopus)

Abstract

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 but 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 (much) smaller than the process dimension. We characterize the required sample size such that the proposed selection method is successful with high probability.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
PublisherIEEE
Pages5920-5924
Number of pages5
ISBN (Electronic)9781509041176
DOIs
Publication statusPublished - 16 Jun 2017
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Acoustics, Speech, and Signal Processing - New Orleans, United States
Duration: 5 Mar 20179 Mar 2017

Conference

ConferenceIEEE International Conference on Acoustics, Speech, and Signal Processing
Abbreviated titleICASSP
CountryUnited States
CityNew Orleans
Period05/03/201709/03/2017

Keywords

  • conditional variance testing
  • graphical model selection
  • high-dimensional statistics
  • Sparsity

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