Inference based on Dual Generalized Order Statistics from Unit Teissier Distribution

  • Zakir Ali
  • Zaki Anwar
  • Nadeem Ahmad
  • Iftkhar Khan
DOI: https://doi.org/10.19139/soic-2310-5070-3137
Keywords: Dual generalized order statistics, record values, single moments, recurrence relations, characterization, Unit Teissier distribution

Abstract

In this study, we utilize dual generalized order statistics (dgos) to explore the moment characteristics of the unitTeissier (UT) distribution. Using this method, we obtain exact and explicit expressions for single moments and developrecurrence relations for both single and product moments. Furthermore, we provide a characterization of the UT distribution,along with additional results related to the moments of record values and reversed order statistics (os). We estimate theunknownparameter of the UTdistribution using the maximum likelihood estimation (MLE) based on dgos. The effectivenessof the derived maximum likelihood estimates (MLEs) is assessed through extensive simulation studies, which emphasizevarious moments and their corresponding relative mean squared errors (MSEs). This research enhances the understanding ofthe UT distribution’s properties and offers valuable insights into its parameter estimation using dgos.
Published
2026-04-21
How to Cite
Zakir Ali, Zaki Anwar, Nadeem Ahmad, & Iftkhar Khan. (2026). Inference based on Dual Generalized Order Statistics from Unit Teissier Distribution. Statistics, Optimization & Information Computing. https://doi.org/10.19139/soic-2310-5070-3137
Issue
Online First
Section
Research Articles
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