Sarmanov bivariate distribution: dependence structure—Fisher information in order statistics and their concomitants

Faculty Science Year: 2022
Type of Publication: ZU Hosted Pages:
Authors:
Metwally AlAwadi Elsayed AlAwadi
Journal: Bulletin of the Malaysian Mathematical Sciences Society Springer Nature Volume:
Keywords : Sarmanov bivariate distribution: dependence structure—Fisher information    
Abstract:
The Sarmanov family of bivariate distributions is considered as the most flexible and efficient extended families of the traditional Farlie–Gumbel–Morgenstern family. The goal of this work is twofold. The first part focuses on revealing some novel aspects of the Sarmanov family’s dependency structure. In the second part, we study the Fisher information (FI) related to order statistics (OSs) and their concomitants about the shape-parameter of the Sarmanov family. The FI helps finding information contained in singly or multiply censored bivariate samples from the Sarmanov family. In addition, the FI about the mean and shape parameter of exponential and power distributions in concomitants of OSs is evaluated, respectively. Finally, the cumulative residual FI in the concomitants of OSs based on the Sarmanov family is derived.
    
     
 
       

Author Related Publications

  • Metwally AlAwadi Elsayed AlAwadi, "Asymptotic Properties of Multivariate Order Statistics with Random Index", .Bull. Malays. Math. Sci. Soc, 2015 More
  • Metwally AlAwadi Elsayed AlAwadi, "Sarmanov Family of Bivariate Distributions: Statistical Properties—Concomitants of Order Statistics—Information Measures", ٍSpringer, 2022 More
  • Metwally AlAwadi Elsayed AlAwadi, "ON CONCOMITANTS OF GENERALIZED ORDER STATISTICS FROM GENERALIZED FGM FAMILY UNDER A GENERAL SETTING", De Gruyter, 2022 More
  • Metwally AlAwadi Elsayed AlAwadi, "Information measures in records and their concomitants arising from Sarmanov family of bivariate distributions", Elsevier, 2022 More
  • Metwally AlAwadi Elsayed AlAwadi, "Limit theorems for random maximum of independent and non-identically distributed random vectors", Statistics, 2013 More

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