| Abstrak |
: |
Standard control charts are based on the assumption that the observations are normally distributed.
In practice, normality often fails and consequently the false alarm rate is seriously in
error. Application of a nonparametric approach is only possible with many Phase I observations.
Since nowadays such very large sample sizes are usually not available, there is need for an
intermediate approach by considering a larger parametric model containing the normal family
as a submodel. In this paper control limits are presented in such larger parametric models,
with emphasis on the so called normal power family. Correction terms are derived, taking into
account that the parameters are estimated. Simulation results show that the control limits
are accurate, not only in the considered parametric family, but also for common distributions
outside the parametric family, thus covering a broad class of distributions. |
