@inproceedings{3f2a576e6f3e426eb3bea243293b90e2,
title = "Fundamental estimation limits in autoregressive processes with compressive measurements",
abstract = "We consider the problem of estimating the parameters of a vector autoregressive (VAR) process from low-dimensional random projections of the observations. This setting covers the cases where we take compressive measurements of the observations or have limits in the data acquisition process associated with the measurement system and are only able to subsample. We first present fundamental bounds on the convergence of any estimator for the covariance or state-transition matrices with and without considering structural constraints of sparsity and low-rankness. We then construct an estimator for these matrices or the parameters of the VAR process and show that it is order optimal.",
keywords = "Autoregressive processes, Covariance estimation, High-dimensional analysis, Minimax theory, Robust estimation, System identification",
author = "Milind Rao and Tara Javidi and Eldar, \{Yonina C.\} and Andrea Goldsmith",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE International Symposium on Information Theory, ISIT 2017 ; Conference date: 25-06-2017 Through 30-06-2017",
year = "2017",
month = aug,
day = "9",
doi = "10.1109/ISIT.2017.8007059",
language = "English",
series = "IEEE International Symposium on Information Theory - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2895--2899",
booktitle = "2017 IEEE International Symposium on Information Theory, ISIT 2017",
}