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A change-time hazard rate model and its goodness of fit

Author

Listed:
  • Bhupendra Singh

    (C.C.S. University)

  • Shubhi Rathi

    (C.C.S. University)

  • Gajraj Singh

    (C.C.S. University)

  • Puneet Kumar Gupta

    (The ICFAI University)

Abstract

The study presents the estimation of the location of the change in the hazard-rate function of the survival times of patients who continue to live after a major life-threatening medical operation. The real data set that acts as a backbone for the present work is the Stanford heart transplantation data. On studying and interpreting, the non-parametric hazard rate plot, and cumulative hazard curve of the data, a suitable hazard rate model is proposed. The parameters involved in the proposed model have been assessed by the classical and Bayesian methods estimation. The likelihood-ratio test has been conducted to test the validity of the change time point in the data.

Suggested Citation

  • Bhupendra Singh & Shubhi Rathi & Gajraj Singh & Puneet Kumar Gupta, 2022. "A change-time hazard rate model and its goodness of fit," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(4), pages 1903-1912, August.
    • Handle: RePEc:spr:ijsaem:v:13:y:2022:i:4:d:10.1007_s13198-021-01601-1
    DOI: 10.1007/s13198-021-01601-1
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    References listed on IDEAS

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    1. Bing Wang & Xiaoguang Wang & Lixin Song, 2020. "Estimation in the single change-point hazard function for interval-censored data with a cure fraction," Journal of Applied Statistics, Taylor & Francis Journals, vol. 47(2), pages 231-247, January.
    2. Kaushik Patra & Dipak Dey, 2002. "A General Class of Change Point and Change Curve Modeling for Life Time Data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 54(3), pages 517-530, September.
    3. Frobish, Daniel & Ebrahimi, Nader, 2009. "Parametric estimation of change-points for actual event data in recurrent events models," Computational Statistics & Data Analysis, Elsevier, vol. 53(3), pages 671-682, January.
    4. Gürler, Ülkü & Deniz Yenigün, C., 2011. "Full and conditional likelihood approaches for hazard change-point estimation with truncated and censored data," Computational Statistics & Data Analysis, Elsevier, vol. 55(10), pages 2856-2870, October.
    5. Sertkaya Karasoy, Durdu & Kadilar, Cem, 2007. "A new Bayes estimate of the change point in the hazard function," Computational Statistics & Data Analysis, Elsevier, vol. 51(6), pages 2993-3001, March.
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    Cited by:

    1. R. Dhanya Nair & E. I. Abdul Sathar, 2024. "Nonparametric estimation of extropy based measures under right censoring," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(6), pages 2374-2382, June.

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