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Analysis of survival data by a Weibull-generalized Poisson distribution

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  • Ramesh C. Gupta
  • Jie Huang

Abstract

In life-testing and survival analysis, sometimes the components are arranged in series or parallel system and the number of components is initially unknown. Thus, the number of components, say Z , is considered as random with an appropriate probability mass function. In this paper, we model the survival data with baseline distribution as Weibull and the distribution of Z as generalized Poisson, giving rise to four parameters in the model: increasing, decreasing, bathtub and upside bathtub failure rates. Two examples are provided and the maximum-likelihood estimation of the parameters is studied. Rao's score test is developed to compare the results with the exponential Poisson model studied by Kus [17] and the exponential-generalized Poisson distribution with baseline distribution as exponential and the distribution of Z as generalized Poisson. Simulation studies are carried out to examine the performance of the estimates.

Suggested Citation

  • Ramesh C. Gupta & Jie Huang, 2014. "Analysis of survival data by a Weibull-generalized Poisson distribution," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(7), pages 1548-1564, July.
    • Handle: RePEc:taf:japsta:v:41:y:2014:i:7:p:1548-1564
    DOI: 10.1080/02664763.2014.881785
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