期刊
AXIOMS
卷 12, 期 9, 页码 -出版社
MDPI
DOI: 10.3390/axioms12090887
关键词
Bayesian nonparametric inference; entropy; extropy information theory; goodness-of-fit tests
This paper presents a novel methodology for computing varentropy and varextropy, drawing inspiration from Bayesian nonparametric methods. The approach is implemented using a computational algorithm in R and its effectiveness is demonstrated across various examples. Furthermore, these new estimators are applied to test uniformity in data.
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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