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

A. A. Modhesh; G. A. Abd-Elmougod

doi:doi:10.11648/j.ajtas.20150406.33

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

A. A. Modhesh, G. A. Abd-Elmougod

Published in American Journal of Theoretical and Applied Statistics (Volume 4, Issue 6)

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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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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TY  - JOUR
T1  - Analysis of Progressive First-Failure-Censoring for Non-normal Model Using Competing Risks Data
AU  - A. A. Modhesh
AU  - G. A. Abd-Elmougod
Y1  - 2015/12/14
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DO  - 10.11648/j.ajtas.20150406.33
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 610
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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.
VL  - 4
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Author Information

A. A. Modhesh

Department of Mathematics, Faculty of Science, Taiz University, Taiz, Yemen
G. A. Abd-Elmougod

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

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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

Copy | Download

@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}
}

Copy | Download

TY  - JOUR
T1  - Analysis of Progressive First-Failure-Censoring for Non-normal Model Using Competing Risks Data
AU  - A. A. Modhesh
AU  - G. A. Abd-Elmougod
Y1  - 2015/12/14
PY  - 2015
N1  - https://doi.org/10.11648/j.ajtas.20150406.33
DO  - 10.11648/j.ajtas.20150406.33
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 610
EP  - 618
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20150406.33
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.
VL  - 4
IS  - 6
ER  -

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