@inproceedings{928b23ffe0ed4bd19f94d4009fab7ea7,
title = "Monotonicity of the Trace-Inverse of Covariance Submatrices and Two-Sided Prediction",
abstract = "It is common to assess the {"}memory strength{"}of a stationary process by looking at how fast the normalized log- determinant of its covariance submatrices (i.e., entropy rate) decreases. In this work, we propose an alternative characterization in terms of the normalized trace-inverse of the covariance submatrices. We show that this sequence is monotonically non-decreasing and is constant if and only if the process is white. Furthermore, while the entropy rate is associated with one-sided prediction errors (present from past), the new measure is associated with two-sided prediction errors (present from past and future). Minimizing this measure is then used as an alternative to Burg's maximum-entropy principle for spectral estimation.",
author = "Anatoly Khina and Arie Yeredor and Ram Zamir",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Symposium on Information Theory, ISIT 2022 ; Conference date: 26-06-2022 Through 01-07-2022",
year = "2022",
doi = "https://doi.org/10.1109/ISIT50566.2022.9834592",
language = "الإنجليزيّة",
series = "IEEE International Symposium on Information Theory - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "450--455",
booktitle = "2022 IEEE International Symposium on Information Theory, ISIT 2022",
address = "الولايات المتّحدة",
}