"Finite-sample bounds on the accuracy of plug-in estimators of fisher i" by Wei Cao, Alex Dytso et al.
 

Finite-sample bounds on the accuracy of plug-in estimators of fisher information

Document Type

Article

Publication Date

5-1-2021

Abstract

Finite-sample bounds on the accuracy of Bhattacharya’s plug-in estimator for Fisher information are derived. These bounds are further improved by introducing a clipping step that allows for better control over the score function. This leads to superior upper bounds on the rates of convergence, albeit under slightly different regularity conditions. The performance bounds on both estimators are evaluated for the practically relevant case of a random variable contaminated by Gaussian noise. Moreover, using Brown’s identity, two corresponding estimators of the minimum mean-square error are proposed.

Identifier

85105806933 (Scopus)

Publication Title

Entropy

External Full Text Location

https://doi.org/10.3390/e23050545

e-ISSN

10994300

Issue

5

Volume

23

Grant

CCF-1908308

Fund Ref

National Science Foundation

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