@inproceedings{2ceb94a017de4c87995166dcff31bf0b,
title = "Charrelation-based estimation of the parameters of non-Gaussian autoregressive processes",
abstract = "Charrelation matrices are similar in structure (and in additional properties) to correlation matrices, and are closely related to Hessians of the log-characteristic function at selected processing-points away from the origin. Charrelation-based estimation methods were shown to offer significant improvement over second-order (correlation-based) methods when the latter are suboptimal. However, judicious selection of the processing-points is required in order to achieve such improvement. In the context of estimating the parameters of an autoregressive process, we present here a method for proper data-driven selection of the processing-points, finding the one which minimizes the predicted mean square estimation error. The resulting performance improvement over classical competing methods is demonstrated in simulation.",
author = "Alon Slapak and Arie Yeredor",
year = "2012",
doi = "https://doi.org/10.1109/SSP.2012.6319728",
language = "الإنجليزيّة",
isbn = "9781467301831",
series = "2012 IEEE Statistical Signal Processing Workshop, SSP 2012",
pages = "448--451",
booktitle = "2012 IEEE Statistical Signal Processing Workshop, SSP 2012",
note = "2012 IEEE Statistical Signal Processing Workshop, SSP 2012 ; Conference date: 05-08-2012 Through 08-08-2012",
}