@inproceedings{4f766af4d6a24f8aa4c4595da8c5fcf6,
title = "Lower Bounds on Non-Bayesian Parameter Estimation Errors under Reparameterization",
abstract = "This paper introduces a comprehensive approach for evaluating non-Bayesian lower bounds on the mean-squared-error in unbiased estimation of a parameter vector, for the special case where the probability density function of the measurements is given as a function of another parameter vector, such that a defined functional relation exists between the two vectors. We study two variations of these bounds and pinpoint the conditions governing the existence of each version. Subsequently, we establish connections between the bounds, showing that when both exist, one is tighter than the other. We also compare them with the Cram{\'e}r-Rao bound, which could have been directly derived, given the availability of the appropriate probability density function. The paper concludes by presenting specific examples relevant to the multidimensional statistical signal processing community. The paper's results help in choosing the tightest possible bound for a given application.",
keywords = "Cram{\'e}r-Rao bound, non-Bayesian parameter estimation, performance bounds, reparameterization",
author = "Shay Sagiv and Hagit Messer and Habi, {Hai Victor} and Joseph Tabrikian",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 13rd IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2024 ; Conference date: 08-07-2024 Through 11-07-2024",
year = "2024",
month = jan,
day = "1",
doi = "10.1109/SAM60225.2024.10636614",
language = "English",
series = "Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop",
publisher = "Institute of Electrical and Electronics Engineers",
booktitle = "2024 IEEE 13rd Sensor Array and Multichannel Signal Processing Workshop, SAM 2024",
address = "United States",
}