On Parametric Misspecified Bayesian Cramér-Rao Bound: An Application to Linear/Gaussian Systems | IEEE Conference Publication | IEEE Xplore

On Parametric Misspecified Bayesian Cramér-Rao Bound: An Application to Linear/Gaussian Systems


Abstract:

A lower bound is an important tool for predicting the performance that an estimator can achieve under a particular statistical model. Bayesian bounds are a kind of such b...Show More

Abstract:

A lower bound is an important tool for predicting the performance that an estimator can achieve under a particular statistical model. Bayesian bounds are a kind of such bounds which not only utilizes the observation statistics but also includes the prior model information. In reality, however, the true model generating the data is either unknown or simplified when deriving estimators, which motivates the works to derive estimation bounds under modeling mismatch situations. This paper provides a derivation of a Bayesian Cramér-Rao bound under model misspecification, by introducing important concepts such as the pseudotrue parameter in a Bayesian context which was not identified in previous works. The general result is particularized in linear and Gaussian problems, where closed-forms are available and results are used to validate the results.
Date of Conference: 04-10 June 2023
Date Added to IEEE Xplore: 05 May 2023
ISBN Information:

ISSN Information:

Conference Location: Rhodes Island, Greece

References

References is not available for this document.