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Nonparametric estimation of regression parameters from censored data with a discrete covariate

Author

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  • Rahbar, Mohammad H.
  • Gardiner, Joseph C.

Abstract

A noniterative method of estimation is presented in a simple linear regression model where the independent variable (covariate) assumes only a finite number of values and the dependent variable (response) is randomly right censored. The censoring distribution may depend on the covariate values. The efficiency of our estimator is compared with another noniterative estimator in the literature using simulations. The asymptotic normality of the estimators of the regression parameters are established. In addition, the distribution of estimator of the asymptotic variance is obtained.

Suggested Citation

  • Rahbar, Mohammad H. & Gardiner, Joseph C., 1995. "Nonparametric estimation of regression parameters from censored data with a discrete covariate," Statistics & Probability Letters, Elsevier, vol. 24(1), pages 13-20, July.
    • Handle: RePEc:eee:stapro:v:24:y:1995:i:1:p:13-20
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    Citations

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    Cited by:

    1. Müller, Ursula U. & Schick, Anton & Wefelmeyer, Wolfgang, 2015. "Estimators in step regression models," Statistics & Probability Letters, Elsevier, vol. 100(C), pages 124-129.
    2. Mohammad H Rahbar & Sangbum Choi & Chuan Hong & Liang Zhu & Sangchoon Jeon & Joseph C Gardiner, 2018. "Nonparametric estimation of median survival times with applications to multi-site or multi-center studies," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-15, May.

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