Asymptotic and finite-sample distributions of one- and two-sample empirical relative entropy, with application to change-point detection

Matthieu Garcin () and Louis Perot
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Matthieu Garcin: DVRC - De Vinci Research Center - DVHE - De Vinci Higher Education
Louis Perot: DMA - Département de Mathématiques et Applications - ENS-PSL (UMR8553) - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique

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Abstract: Relative entropy, as a divergence metric between two distributions, can be used for offline change-point detection and extends classical methods that mainly rely on moment-based discrepancies. To build a statistical test suitable for this context, we study the distribution of empirical relative entropy and derive several types of approximations: concentration inequalities for finite samples, asymptotic distributions, and Berry-Esseen bounds in a pre-asymptotic regime. For the latter, we introduce a new approach to obtain Berry-Esseen inequalities for nonlinear functions of sum statistics under some convexity assumptions. Our theoretical contributions cover both one-and two-sample empirical relative entropies. We then detail a change-point detection procedure built on relative entropy and compare it, through extensive simulations, with classical methods based on moments or on information criteria. Finally, we illustrate its practical relevance on two real datasets involving temperature series and volatility of stock indices.

Keywords: Berry-Esseen bounds; concentration inequalities; information theory; Kullback-Leibler divergence; structural break detection; two-sample divergence testing (search for similar items in EconPapers)
Date: 2025-12-18
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