The first part of this talk describes and compares different procedures to compute confidence intervals for parameters and quantiles of the Weibull distribution for Type I censored data. The methods can be classified into three groups: the commonly-used normal approximation for the distribution of (a possibly transformed) studentized maximum likelihood estimator, methods based on the likelihood ratio statistic and its modifications, and parametric bootstrap methods, including the use of bootstrap-type simulation to calibrate the procedures in the first two groups. We use the Monte Carlo simulation to investigate the finite sample properties of these procedures. Exceptional cases, that are due to discreteness in the Type I censoring, are noted.

The second part of this talk presents extensions of previous work by Cheng and Iles (1983, 1988) for construction of simultaneous confidence bands on a cdf for continuous parametric model from complete and censored data using standard large-sample approximations. These methods are extended to corresponding simulation or bootstrap-calibrated versions of the same methods. Both two-sided and one-sided simultaneous confidence bands for location-scale models are discussed and compared including situations with complete and censored data. We illustrate the implementation of the methods with an application to estimate probability of detection (POD) used to asses nondestructive evaluation (NDE) capability.