On the sample complexity of graphical model selection from non-stationary samples

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Abstract

We characterize the sample size required for accurate graphical model selection from non-stationary samples. The observed samples are modeled as a zero-mean Gaussian random process whose samples are uncorrelated but have different covariance matrices. This includes the case where observations form stationary or underspread processes. We derive a sufficient condition on the required sample size by analyzing a simple sparse neighborhood regression method.

Details

Original languageEnglish
Title of host publication2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
Publication statusPublished - 10 Sep 2018
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Acoustics, Speech, and Signal Processing - Calgary, Canada
Duration: 15 Apr 201820 Apr 2018

Conference

ConferenceIEEE International Conference on Acoustics, Speech, and Signal Processing
Abbreviated titleICASSP
CountryCanada
CityCalgary
Period15/04/201820/04/2018

    Research areas

  • Graphical model selection, High-dimensional statistics, Neighborhood regression, Sparsity

ID: 30110503