Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Khudania, Harshita; Rajesh, Richu; Raju, David Chris Title A name given to the resource On estimation of extropy for non-negative data with application on uniformity testing Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Journal of Statistical Computation and Simulation;Volume;96;Issue;7;pp.1709-1727 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1080/00949655.2025.2605498" target="_blank" rel="noreferrer noopener">https://doi.org/10.1080/00949655.2025.2605498</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105025469866?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105025469866?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Khudania H., Department of Statistics and Data Science, Christ (Deemed to be University), Karnataka, Bengaluru, India; Rajesh R., Department of Statistics, Government Victoria College, Kerala, Palakkad, India; Raju D.C., Department of Statistics, Nirmala College Muvattupuzha, Kerala, Ernakulam, India Description An account of the resource {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. 2025 Informa UK Limited, trading as Taylor &amp; Francis Group. Subject The topic of the resource Extropy; Kernel estimator; mean squared error; Poisson weights; uniform testing Publisher An entity responsible for making the resource available Taylor and Francis Ltd. Relation A related resource ISSN: 949655; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online