Relative information spectra with applications to statistical inference

Relative information spectra with applications to statistical inference

For any pair of probability measures defined on a common space, their relative information spectra——specifically, the distribution functions of the loglikelihood ratio under either probability measure——fully encapsulate all that is relevant for distinguishing them. This paper explores the properties...

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Bibliographic Details
Main Author: Sergio Verdú
Format: Article
Language:English
Published: AIMS Press 2024-12-01
Series:AIMS Mathematics
Subjects:
information theory
statistical inference
sufficient statistics
hypothesis testing
neyman-pearson tests
information spectrum method
relative entropy
kullback-leibler divergence
$ f $-divergence
total variation distance
bhattacharyya distance
Online Access:https://www.aimspress.com/article/doi/10.3934/math.20241668
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https://www.aimspress.com/article/doi/10.3934/math.20241668

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