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Avoiding Problems With Normal Approximation Confidence Intervals for Probabilities

Summary: [This abstract is based on the authors' abstract.] Normal approximation confidence interval procedures (NACPs) are widely used in the analysis of censored data because the confidence intervals are easy to compute and explain, but when the sample size is small or there is heavy censoring, performance may be poor. A transformation can be applied to improve performance, but the degree of improvement depends on the chosen function. This study compares different NACPs for distribution probabilities. Results show that an NACP based on a studentized statistic is preferable to alternative NACPs.

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  • Topics: Engineering
  • Keywords: Asymptotics, Approximation, Censored data, Lifetime data, Scale parameter, Location parameter, Maximum likelihood estimate (MLE), Transformation
  • Author: Hong, Yili; Meeker, William Q.; Escobar, Luis A.
  • Journal: Technometrics