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Analysis of Progressive First-Failure-Censoring for Non-normal Model Using Competing Risks Data

Received: 8 September 2015     Accepted: 7 October 2015     Published: 14 December 2015
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Abstract

Competing risks data usually arises in studies in which the death or failure of an individual or an item may be classified into one of T≥2 mutually exclusive causes. In this paper, we will study the competing risks model when the data is progressively first-failure-censored. Based on this type of censoring, we derive the maximum likelihood estimators (MLE's) for the unknown parameters. Approximate confidence intervals and two bootstrap confidence intervals are also proposed. The results in the cases of first-failure censoring, progressive Type II censoring, Type II censoring and complete sample are special cases. A real data set has been analyzed for illustrative purposes. Different methods have been compared using Monte Carlo simulations.

Published in American Journal of Theoretical and Applied Statistics (Volume 4, Issue 6)
DOI 10.11648/j.ajtas.20150406.33
Page(s) 610-618
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2015. Published by Science Publishing Group

Keywords

Burr XII Distribution, Progressive First-Failure-Censoring, Competing Risks, Maximum Likelihood Method, Bootstrap

References
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[14] A. A. Soliman, A. H. Abd Ellah, N. A. Abou-Elheggag, G. A. Abd-Elmougod, Estimation of the parameters of life for Gompertz distribution using progressive first-failure censored data. Computational Statistics and Data Analysis. 56, (2012), 2471-2485.
[15] A. A. Soliman, A. H. Abd Ellah, N. A. Abou-Elheggag, A. A. Modhesh, Estimation of the coefficient of variation for non-normal model using progressive first-failure-censoring data. Journal of Applied Statistics. 39(12), (2012), 2741-2758.
[16] A. A. Soliman, A. H. Abd Ellah, N. A. Abou-Elheggag, A. A. Modhesh, Estimation from Burr type XII distribution using progressive first-failure censored data. Journal of Statistical Computation and Simulation. 83(12), (2013), 2270-2290.
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Cite This Article
  • APA Style

    A. A. Modhesh, G. A. Abd-Elmougod. (2015). Analysis of Progressive First-Failure-Censoring for Non-normal Model Using Competing Risks Data. American Journal of Theoretical and Applied Statistics, 4(6), 610-618. https://doi.org/10.11648/j.ajtas.20150406.33

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    ACS Style

    A. A. Modhesh; G. A. Abd-Elmougod. Analysis of Progressive First-Failure-Censoring for Non-normal Model Using Competing Risks Data. Am. J. Theor. Appl. Stat. 2015, 4(6), 610-618. doi: 10.11648/j.ajtas.20150406.33

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    AMA Style

    A. A. Modhesh, G. A. Abd-Elmougod. Analysis of Progressive First-Failure-Censoring for Non-normal Model Using Competing Risks Data. Am J Theor Appl Stat. 2015;4(6):610-618. doi: 10.11648/j.ajtas.20150406.33

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  • @article{10.11648/j.ajtas.20150406.33,
      author = {A. A. Modhesh and G. A. Abd-Elmougod},
      title = {Analysis of Progressive First-Failure-Censoring for Non-normal Model Using Competing Risks Data},
      journal = {American Journal of Theoretical and Applied Statistics},
      volume = {4},
      number = {6},
      pages = {610-618},
      doi = {10.11648/j.ajtas.20150406.33},
      url = {https://doi.org/10.11648/j.ajtas.20150406.33},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20150406.33},
      abstract = {Competing risks data usually arises in studies in which the death or failure of an individual or an item may be classified into one of T≥2 mutually exclusive causes. In this paper, we will study the competing risks model when the data is progressively first-failure-censored. Based on this type of censoring, we derive the maximum likelihood estimators (MLE's) for the unknown parameters. Approximate confidence intervals and two bootstrap confidence intervals are also proposed. The results in the cases of first-failure censoring, progressive Type II censoring, Type II censoring and complete sample are special cases. A real data set has been analyzed for illustrative purposes. Different methods have been compared using Monte Carlo simulations.},
     year = {2015}
    }
    

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    AB  - Competing risks data usually arises in studies in which the death or failure of an individual or an item may be classified into one of T≥2 mutually exclusive causes. In this paper, we will study the competing risks model when the data is progressively first-failure-censored. Based on this type of censoring, we derive the maximum likelihood estimators (MLE's) for the unknown parameters. Approximate confidence intervals and two bootstrap confidence intervals are also proposed. The results in the cases of first-failure censoring, progressive Type II censoring, Type II censoring and complete sample are special cases. A real data set has been analyzed for illustrative purposes. Different methods have been compared using Monte Carlo simulations.
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Author Information
  • Department of Mathematics, Faculty of Science, Taiz University, Taiz, Yemen

  • Department of Mathematics, Faculty of Science, Taif University, KSA

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