An attractive way to control attribute data from high quality processes is to wait till r $\geq$ 1 failures have occurred. The choice of r in such negative binomial charts is dictated by how much the failure rate is supposed to change during Out-of-Control. However, these results have been derived for the case of homogeneous data. Especially in health care monitoring, (groups of) patients will often show large heterogeneity. In the present paper we will show how such overdispersion can be taken into account. In practice, typically neither the average failure rate, nor the overdispersion parameter(s), will be known. Hence we shall also derive and analyze the estimated version of the new chart.
|Name||Memorandum / Department of Applied Mathematics|
|Publisher||Department of Applied Mathematics, University of Twente|
- Average run length
- Statistical Process Control
- Estimated parameters
- High quality processes
- Geometric charts