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On the Spectral Estimation of VARMA Processes
arXiv stat.ML0 views
arXiv:2611.13973v1 Announce Type: new Abstract: We consider the problem of estimating the power spectral density of a Vector-valued Autoregressive Moving-Average (VARMA) process. A well-established approach to accomplishing this task consists of formulating a moment problem that selects the spectral density maximizing an entropy-like index while matching certain moments of the underlying process. However, existing methods assume that the moving-average (MA) component is either fixed or scalar, thereby reducing the class of ARMA models over which the optimal spectral density is sought. In this paper, we propose a new moment problem, based on the notion of transportation entropy, for learning the spectral density of a VARMA process without fixing the MA component or restricting it to be scalar.
Source：https://arxiv.org/abs/2611.13973PreviousOutliers and the Hard-Margin Linear SVM in High-Dimension, Low-Sample-Size SettingsNextCompositional random forest
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