Inaccuracy and divergence measures based on survival extropy, their properties, and applications in testing and image analysis

TL;DR

This paper develops new inaccuracy and divergence measures based on survival extropy, explores their properties, and demonstrates their applications in image analysis, goodness-of-fit testing, distribution characterization, and failure time data analysis.

Contribution

It introduces novel symmetric divergence measures based on survival extropy, along with their dynamic forms, and applies them to various statistical and data analysis problems.

Findings

Proposed new symmetric divergence measures with desirable properties.

Developed a goodness-of-fit test for the uniform distribution.

Validated estimators through simulation studies and demonstrated practical applications.

Abstract

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.