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Asymptotic Properties of a Statistical Estimator of the Jeffreys Divergence: The Case of Discrete Distributions

Mathematics. 2024. Vol. 12. No. 21. Article 3319.
Glinskiy V., Artem Logachov, Logachova O., Rojas H., Serga L., Yambartsev A.

We investigate the asymptotic properties of the plug-in estimator for the Jeffreys divergence, the symmetric variant of the Kullback–Leibler (KL) divergence. This study focuses specifically on the divergence between discrete distributions. Traditionally, estimators rely on two independent samples corresponding to two distinct conditions. However, we propose a one-sample estimator where the condition results from a random event. We establish the estimator’s asymptotic unbiasedness (law of large numbers) and asymptotic normality (central limit theorem). Although the results are expected, the proofs require additional technical work due to the randomness of the conditions.

Research target: Mathematics
Language: English
DOI
Keywords: central limit theoremJeffreys Divergence
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