General results on bivariate extended Weibull Morgenstern family and concomitants of its generalized order statistics

Jafari, Ali Akbar; Almaspoor, Zahra; Tahmasebi, Saeid

doi:10.1007/s11587-021-00680-3

Cited by 6 publications

(2 citation statements)

References 29 publications

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“…Assume that the random vector (T, W) follows the extended Weibull family (denoted by EWF). As mentioned in [30], the EWF has its CDF and PDF described as follows…”

Section: The Marginal Distribution Of Ckrv Based On Efgm(cd)mentioning

confidence: 99%

Extropy and Some of Its More Recent Related Measures for Concomitants of K-Record Values in an Extended FGM Family

Abd Elgawad,

Barakat,

Alawady

et al. 2023

Mathematics

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This study uses an effective, recently extended Farlie–Gumbel–Morgenstern (EFGM) family to derive the distribution of concomitants of K-record upper values (CKRV). For this CKRV, the negative cumulative residual extropy (NCREX), weighted NCREX (WNCREX), negative cumulative extropy (NCEX), and weighted NCEX (WNCEX) are theoretically and numerically examined. This study presents several beautiful symmetrical and asymmetric relationships that these inaccuracy measurements satisfy. Additionally, empirical estimations are provided for these measures, and their visualizations enable users to verify their accuracy.

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“…Assume that the random vector (T, W) follows the extended Weibull family (denoted by EWF). As mentioned in [30], the EWF has its CDF and PDF described as follows…”

Section: The Marginal Distribution Of Ckrv Based On Efgm(cd)mentioning

confidence: 99%

Extropy and Some of Its More Recent Related Measures for Concomitants of K-Record Values in an Extended FGM Family

Abd Elgawad,

Barakat,

Alawady

et al. 2023

Mathematics

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“…Furthermore, Alawady et al [7] studied the concomitants of m-generalized OSs from the Cambanis family under general setting. For more recent works about this subject see Abd Elgawad and Alawady [1], Abd Elgawad et al [2][3][4][5], Alawady et al [7][8][9], Barakat et al [14,16], Jafari et al [22], and Tahmasebi et al [35,36].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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.

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Second-order concomitants of order statistics from bivariate Cambanis family: some information measures–estimation

Barakat,

Alawady,

Taher

et al. 2024

Communications in Statistics - Simulation and Computation

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General results on bivariate extended Weibull Morgenstern family and concomitants of its generalized order statistics

Cited by 6 publications

References 29 publications

Extropy and Some of Its More Recent Related Measures for Concomitants of K-Record Values in an Extended FGM Family

Extropy and Some of Its More Recent Related Measures for Concomitants of K-Record Values in an Extended FGM Family

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

Second-order concomitants of order statistics from bivariate Cambanis family: some information measures–estimation

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