
SAS, JMP, R-”Survival”, Minitab, ReliaSoft, XLStat, and perhaps other statistics programs offer the Kaplan-Meier nonparametric reliability estimator as a default. Take credit for using nonparametric reliability estimation and avoiding unwarranted assumptions. What could go wrong using the Kaplan-Meier estimator?
- Cohorts could be non-stationary, random processes!
- Failures could be recurrent process counts, not dead-forever!
- Lifetime data depends on the censoring process(es); e.g., competing risks!
- Greenwood’s variance estimator errs! Covariances are missing!
- Alternative estimators could be more efficient than Kaplan-Meier!
Are you using all the information in data available from population data required by GAAP? If you don’t have lifetime data, use periodic failure counts. This article describes an example where the Kaplan-Meier estimator from grouped lifetime data is less efficient than using periodic failure counts, even though you don’t know which cohort they came from!
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