Monitoring Industrial Process using a Robust Modified Mean Chart

Detecting outliers in contingency table is an interesting statistical problem and it poses
additional di culties due to the polarization of cell counts. The fundamental de nition of
'markedly deviant' cell as an outlier is clearly exploited in this study by introducing a pivot
element to capture the deviations. The present study considers a two-step con rmatory
procedure to detect outliers in I   J contingency table. The procedure deals with (i)
identifying the reliable set of candidate outliers using the deviation from the pivot element
and then (ii) detect those set of outlying cells by examining di erent type of residuals of
the suitable  tted model. The robustness of the procedure is investigated through a
simulation study along with applications to real datasets.