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A semi-parametric regression model for analysis of middle censored lifetime data

Author

Listed:
  • S. Rao Jammalamadaka

    (University of California, Santa Barbara - USA)

  • Sundaresan Nair Prasad

    (Cochin University of Science and Technology, Kerala - India)

  • Paduthol Godan Sankaran

    (Cochin University of Science and Technology, Kerala - India)

Abstract

Middle censoring introduced by Jammalamadaka and Mangalam (2003), refers to data arising in situations where the exact lifetime becomes unobservable if it falls within a random censoring interval, otherwise it is observable. In the present paper we propose a semi-parametric regression model for such lifetime data, arising from an unknown population and subject to middle censoring. We provide an algorithm to find the nonparametric maximum likelihood estimator (NPMLE) for regression parameters and the survival function. The consistency of the estimators are established. We report simulation studies to assess the finite sample properties of the estimators. We then analyze a real life data on survival times for diabetic patients studied by Lee et al. (1988).

Suggested Citation

  • S. Rao Jammalamadaka & Sundaresan Nair Prasad & Paduthol Godan Sankaran, 2016. "A semi-parametric regression model for analysis of middle censored lifetime data," Statistica, Department of Statistics, University of Bologna, vol. 76(1), pages 27-40.
    • Handle: RePEc:bot:rivsta:v:76:y:2016:i:1:p:27-40
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    Cited by:

    1. P. G. Sankaran & S. Prasad, 2017. "An Additive Risks Regression Model For Middle-Censored Lifetime Data," Statistics in Transition New Series, Polish Statistical Association, vol. 18(3), pages 459-479, September.
    2. Sankaran P. G. & Prasad S., 2017. "An Additive Risks Regression Model for Middle-Censored Lifetime Data," Statistics in Transition New Series, Polish Statistical Association, vol. 18(3), pages 459-479, September.

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