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On estimation of extropy for non-negative data with application on uniformity testing
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DOI：10.1080/00949655.2025.2605498.png)
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Abstract
En 中文
{Poisson weights-based density estimator is used to estimate the extropy function to the non-negative data}. The traditional class of nonparametric extropy estimators, typically constructed using kernel density estimators with symmetric kernels, is not well suited for non-negative data. To address this limitation, we propose two Poisson-weights-based density estimators that are naturally adapted to the non-negative domain. The asymptotic properties of the proposed estimators are rigorously established, providing theoretical support for their use. A comprehensive simulation study demonstrates that both estimators outperform their conventional kernel-based counterparts in terms of bias and mean squared error. Furthermore, we introduce uniformity tests based on extropy and obtain their critical values through simulation. The practical utility of the proposed methods is illustrated through analyses of real data sets.
Keywords:
Extropy
Kernel estimator
Poisson weights
mean squared error
uniform testing
Journal
J
IF:
1.2
Papers:
114
Citations:
4.1K
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Cited Papers