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Gamma lifetimes and associated inference for interval-censored cure rate model with COM–Poisson competing cause

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  • Piyachart Wiangnak
  • Suvra Pal

Abstract

In this article, we consider a competing cause scenario and assume the wider family of Conway–Maxwell–Poisson (COM–Poisson) distribution to model the number of competing causes. Assuming the type of the data to be interval censored, the main contribution is in developing the steps of the expectation maximization (EM) algorithm to determine the maximum likelihood estimates (MLEs) of the model parameters. A profile likelihood approach within the EM framework is proposed to estimate the COM–Poisson shape parameter. An extensive simulation study is conducted to evaluate the performance of the proposed EM algorithm. Model selection within the wider class of COM–Poisson distribution is carried out using likelihood ratio test and information-based criteria. A study to demonstrate the effect of model mis-specification is also carried out. Finally, the proposed estimation method is applied to a data on smoking cessation and a detailed analysis of the obtained results is presented.

Suggested Citation

  • Piyachart Wiangnak & Suvra Pal, 2018. "Gamma lifetimes and associated inference for interval-censored cure rate model with COM–Poisson competing cause," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 47(6), pages 1491-1509, March.
    • Handle: RePEc:taf:lstaxx:v:47:y:2018:i:6:p:1491-1509
    DOI: 10.1080/03610926.2017.1321769
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    Cited by:

    1. Suvra Pal & Yingwei Peng & Wisdom Aselisewine, 2024. "A new approach to modeling the cure rate in the presence of interval censored data," Computational Statistics, Springer, vol. 39(5), pages 2743-2769, July.
    2. Diego I. Gallardo & Yolanda M. Gómez & Héctor J. Gómez & María José Gallardo-Nelson & Marcelo Bourguignon, 2023. "The Slash Half-Normal Distribution Applied to a Cure Rate Model with Application to Bone Marrow Transplantation," Mathematics, MDPI, vol. 11(3), pages 1-16, January.

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