Advancements in Rényi entropy and divergence estimation for model assessment

Luai Al-Labadi (), Zhirui Chu () and Ying Xu ()
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Luai Al-Labadi: University of Toronto Mississauga
Zhirui Chu: University of Toronto Mississauga
Ying Xu: University of Toronto Mississauga

Computational Statistics, 2025, vol. 40, issue 2, No 2, 633-650

Abstract: Abstract Entropy and divergence, fundamental concepts in machine learning and computer science, have gained significant traction over the past decade. Statisticians have been developing estimators for these measures, advancing computational analysis. In this paper, we present nonparametric estimators for Rényi entropy and divergence. Through a range of examples, we showcase the effectiveness of our approach, demonstrating its applicability across various contexts. Furthermore, we leverage these estimators for model assessment.

Keywords: Entropy; Computational analysis; Information theory; Model assessment; Nonparametric estimation (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s00180-024-01507-z

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