A bilateral bound on the mean-square error for estimation in model mismatch
2023 IEEE International Symposium on Information Theory (ISIT), 2023•ieeexplore.ieee.org
A bilateral (ie, 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 …
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 …
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.
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