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How to calculate the mean life time using censored data?
In this approach, can be computed using the familiar sample mean formula provided the censored values are imputed.
How to estimate population mean using right censored data?
Estimation of the population mean based on right censored observations is considered. The naive sample mean will be an inconsistent and asymptotically biased estimator in this case. An estimate suggested in textbooks is to compute the area under a Kaplan–Meier curve.
How is the Kaplan Meier estimator used in censored data?
The censored observation 7+ will be treated as a 7 (true failure) for computing the Kaplan–Meier estimator so that ( Table 1 ). This leads to an estimated mean of The following two seemingly very different approaches would produce the same correct answer and provide additional insight into the problem of right censored data.
Can a sample mean be used to estimate the mean life time?
Estimation of a population mean by its sample counterpart can perhaps be regarded as the most basic statistical method students learn in their elementary statistics courses. However, the sample mean will no longer work as an estimate of the mean life time if the sample is right censored.
What does suspended or right censored data mean?
With “suspended” or “right censored” data, the unit operated successfully for a known period of time and then continued (or could have continued) to operate for an additional unknown period of time (e.g., the unit was still operating at 100 hours of operation).
When is a failure a right censored observation?
Failures are seen only if they occur before a particular time. A unit surviving longer than that time is considered a right-censored observation. Right-censored data are sometimes time-censored or failure-censored. Time censoring means that you perform the study for a specified period of time.
What happens if a life time is censored?
If a life time is censored by then the censored value is replaced by the conditional expectation of given and that , which equals and can be estimated by Once again, the largest observations will have to be treated as true failures for this calculation.