Abstract
The fairly limited range of tools for multivariate statistical process control generally rests on the assumption that the data vectors follow a multivariate normal distribution - an assumption that is rarely satisfied. We discuss detecting possible shifts in the mean vector of a multivariate measurement of a statistical process when the multivariate distribution of the measurement is non-Gaussian. A nonparametric cumulative sum procedure is suggested which is based both on the order information among the measurement components and on the order information between the measurement components and their in-control means. It is shown that this procedure is effective in detecting a wide range of possible shifts. Several numerical examples are presented to evaluate its performance. This procedure is also applied to a data set from an aluminium smelter.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 151-164 |
| Number of pages | 14 |
| Journal | Journal of the Royal Statistical Society Series D: The Statistician |
| Volume | 52 |
| Issue number | 2 |
| DOIs | |
| State | Published - 2003 |
Keywords
- Antiranks
- Cumulative sum
- Distribution-free procedures
- Order statistics
- Robustness
- Statistical process control