Bootstrap Based Confidence Interval Estimation of Quantiles for Current Status Data


  •  Wei Chen    
  •  Fengling Ren    

Abstract

In this paper, we proposed a bootstrap approach to construct the confidence interval of quantiles for current status data, which is computationally simple and efficient without estimating nuisance parameters. The reasonability of the proposed method is verified by the well performance presented in the extensive simulation study. We also analyzed a real data set as illustration.


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