Minimax Estimation of Partially-Observed Vector AutoRegressions. (arXiv:2106.09327v2 [eess.SP] UPDATED)

Web: http://arxiv.org/abs/2106.09327

High-dimensional time series are a core ingredient of the statistical
modeling toolkit, for which numerous estimation methods are known. But when
observations are scarce or corrupted, the learning task becomes much harder.
The question is: how much harder?

In this paper, we study the properties of a partially-observed Vector
AutoRegressive process, which is a state-space model endowed with a stochastic
observation mechanism. Our goal is to estimate its sparse transition matrix,
but we only have access to a small and …

arxiv minimax vector