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Data Analysis by Adaptive Progressive Hybrid Censored Under Bivariate Model

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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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Data Availability

The data is included in Section Application of Diabetic Nephropathy Data.

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Acknowledgements

The authors wish to thank the editor. We also thank anonymous for their encouragement and support. The authors are grateful to anyone reviewed the paper carefully and for their helpful comments that improve this paper.

Funding

The authors received no specific funding for this study.

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Authors and Affiliations

  1. Faculty of Graduate Studies for Statistical Research, Cairo University, Giza, Egypt

    El-Sayed A. El-Sherpieny, Hiba Z. Muhammed & Ehab M. Almetwally

  2. Department of Statistics, Faculty of Business Administration, Delta University of Science and Technology, Mansoura, Egypt

    Ehab M. Almetwally

Authors
  1. El-Sayed A. El-Sherpieny
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  2. Hiba Z. Muhammed
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  3. Ehab M. Almetwally
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Contributions

Conceptualization by EAES, and HZM; Supervision by EAES, and HZM; Resources by EMA; Software by EMA; Writing and original draft by EMA; Writing, review, and editing by EAES, and HZM.

Corresponding author

Correspondence to Ehab M. Almetwally.

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Conflict of interest

The authors declare that they have no conflicts of interest to report regarding the present study.

Ethical Approval

All of the followed procedures were in accordance with the ethical and scientific standards. This article does not contain any studies with human participants performed by the author.

Code Availability

Function “maxlik” of “maxLik” package and “copula” package in the R program has been used.

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El-Sherpieny, ES.A., Muhammed, H.Z. & Almetwally, E.M. Data Analysis by Adaptive Progressive Hybrid Censored Under Bivariate Model. Ann. Data. Sci. 11, 507–548 (2024). https://doi.org/10.1007/s40745-022-00455-z

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