@inproceedings{366d1e452d6a4cd691ebf7562c1ca788,
title = "A Bilateral Bound on the Mean-Square Error for Estimation in Model Mismatch",
abstract = "A bilateral (i.e., upper and lower) bound on the mean-square error under a general model mismatch is developed. The bound, which is derived from the variational representation of the chi-square divergence, is applicable in the Bayesian and nonBayesian frameworks to biased and unbiased estimators. Unlike other classical MSE bounds that depend only on the model, our bound is also estimator-dependent. Thus, it is applicable as a tool for characterizing the MSE of a specific estimator. The proposed bounding technique has a variety of applications, one of which is a tool for proving the consistency of estimators for a class of models. Furthermore, it provides insight as to why certain estimators work well under general model mismatch conditions.",
keywords = "chi-square divergence, model mismatch, Parameter estimation, performance bounds",
author = "Amir Weiss and Alejandro Lancho and Yuheng Bu and Wornell, {Gregory W.}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Symposium on Information Theory, ISIT 2023 ; Conference date: 25-06-2023 Through 30-06-2023",
year = "2023",
doi = "https://doi.org/10.1109/ISIT54713.2023.10206620",
language = "الإنجليزيّة",
series = "IEEE International Symposium on Information Theory - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2655--2660",
booktitle = "2023 IEEE International Symposium on Information Theory, ISIT 2023",
address = "الولايات المتّحدة",
}