Proposed in Chapter 5 of Liu (2012). The original data set will be bootstrapped to obtain an estimate of the mean squared error of the quantile estimates, such that quantile estimates under different subjective censoring thresholds could be compared. We could also obtain the standard error of quantile estimates via bootstrap.
1 2  bootstrapCMLE(dat, qInt = 0.05, canSet = seq(0.1, 0.5, by = 0.1),
B = 5000, randSeed = NULL, conCr = 1e09, nIter = 1000)

dat 
A complete data set without censoring. See 
qInt 
The quantile of interest, e.g. 5% quantile or 10% quantile. 
canSet 
A vector of the candidate subjective censoring thresholds, expressed as the proportion of data smaller it, e.g. 10%, 20%, ..., 100% (noncensoring) 
B 
Number of bootstrap replicate data sets 
randSeed 
The seed for random number generation. If NULL, the random seed will not be set 
conCr 
Convergence criterion for the algorithm to calculate the censored Weibull MLE. See 
nIter 
See 
This function is designed to only work for a complete data set where every observation is fully observed (noncensoring). We could decide the best threshold (proportion) of subjective censoring based on bootstrap. For the advantage of subjective censoring, please see Liu (2012).
This function will call C to do all calculations. So it is recommended that the user should make sure the cenWbMLE.T2
could work for their original data set.
results 
A matrix of length( 
bQEst 
A matrix of Bbylength( 
Please report the numerical problems and inconvenience when using this function to the author.
Yang (Seagle) Liu <yang.liu@stat.ubc.ca>
Liu Y. (2012). Lower Quantile Estimation of Wood Strength Data. Master Thesis, Department of Statistics, UBC. Downloadable here.
bootstrapCenWbMix
1 2 3 4  set.seed(1)
y < sort(rweibull(100, 7, 7))
tlist < bootstrapCMLE(y, B=1000, canSet=c(0.1, 0.5, 1), randSeed=1)
tlist$results #Usually, we only need to look at the results part.

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