This article introduces novel measures of inaccuracy and divergence based on survival extropy and their dynamic forms and explores their properties and applications. To address the drawbacks of asymmetry and range limitations, we introduce two measures: the survival extropy inaccuracy ratio and symmetric divergence measures. The inaccuracy ratio is utilized for the analysis and classification of images. A goodness-of-fit test for the uniform distribution is developed using the survival extropy divergence. Characterizations of the exponential distribution are derived using the dynamic survival extropy inaccuracy and divergence measures. The article also proposes non-parametric estimators for the divergence measures and conducts simulation studies to validate their performance. Finally, it demonstrates the application of symmetric survival extropy divergence in failure time data analysis.

翻译：本文基于生存外熵及其动态形式，提出了新的失准度与散度度量方法，并探讨了其性质与应用。为克服非对称性与取值范围局限的缺陷，我们引入了两种度量：生存外熵失准比与对称散度度量。失准比被应用于图像分析与分类。利用生存外熵散度，我们构建了均匀分布的拟合优度检验。通过动态生存外熵失准度与散度度量，推导了指数分布的特征刻画。本文还提出了散度度量的非参数估计量，并通过模拟研究验证其性能。最后，展示了对称生存外熵散度在失效时间数据分析中的应用。