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Annals of Data Science

Data Analysis by Adaptive Progressive Hybrid Censored Under Bivariate Model

  • 23-10-2022
Published in:
Authors
El-Sayed A. El-Sherpieny
Hiba Z. Muhammed
Ehab M. Almetwally
Published in
Annals of Data Science | Issue 2/2024

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Abstract

The purpose of this paper is to introduce the adaptive progressive hybrid censored scheme of the bivariate model which expands the limited applicability of failure censored schemes for the bivariate models in several fields of products. Also, the paper discusses a new bivariate model based on an adaptive progressive hybrid censored with more efficacy than the traditional models. Based on the FGM copula function and Odd-Weibull family, we will introduce the bivariate FGM Weibull-Weibull distribution. To estimate the model parameters, maximum likelihood and Bayesian estimation are used. In addition, for the parameter model, asymptotic confidence intervals and credible intervals of the highest posterior density for the Bayesian are calculated. A Monte-Carlo simulation analysis is carried out of the maximum likelihood and Bayesian estimators. Finally, we demonstrate the utility of the suggested bivariate distribution using real data from the medical area, such as diabetic nephropathy data.

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previous article Statistical Analysis from the Generalized Inverse Lindley Distribution with Adaptive Type-II Progressively Hybrid Censoring Scheme
next article A New Class of Distribution Over Bounded Support and Its Associated Regression Model
Title
Data Analysis by Adaptive Progressive Hybrid Censored Under Bivariate Model
Authors
El-Sayed A. El-Sherpieny
Hiba Z. Muhammed
Ehab M. Almetwally
Publication date
23-10-2022
Publisher
Springer Berlin Heidelberg
Published in
Annals of Data Science / Issue 2/2024
Print ISSN: 2198-5804
Electronic ISSN: 2198-5812
DOI
https://doi.org/10.1007/s40745-022-00455-z
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