Dispersion indices based on Kerridge inaccuracy measure and Kullback-Leibler divergence

Buono, Francesco; Calì, Camilla; Longobardi, Maria

doi:10.1080/03610926.2023.2222926

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Cited by 6 publications

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“…It is possible to extend the results presented in this paper to study other dispersion indices. For instance, dispersion indices based on Kerridge inaccuracy measure and Kullback-Leibler divergence, as studied by [34], can be explored using a similar Bayesian nonparametric framework.…”

Section: Discussionmentioning

confidence: 99%

Bayesian Estimation of Variance-Based Information Measures and Their Application to Testing Uniformity

Al-Labadi,

Hamlili,

2023

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Entropy and extropy are emerging concepts in machine learning and computer science. Within the past decade, statisticians have created estimators for these measures. However, associated variability metrics, specifically varentropy and varextropy, have received comparably less attention. This paper presents a novel methodology for computing varentropy and varextropy, drawing inspiration from Bayesian nonparametric methods. We implement this approach using a computational algorithm in R and demonstrate its effectiveness across various examples. Furthermore, these new estimators are applied to test uniformity in data.

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Section: Discussionmentioning

confidence: 99%