In this paper, we propose a nonparametric CUSUM control chart for detecting a range of shifts in the location parameters based on previous research. This control chart is dynamically adaptive, ranks method-based nonparametric and self-starting; it can monitor various sizes of shifts in difference distributions simultaneously; and it can be used to monitor processes at the start-up stages. This ...

The scientific literature is abundant regarding control charts in multivariate environments for numerical and mixed data; however, there are few publications qualitative data. Qualitative variables provide valuable information on processes various industrial, productive, technological, health contexts. Social no exception. There multiple nominal ordinal categorical used economics, psychology, l...

Multivariate quality control charts show some advantages to monitor several variables in comparison with the simultaneous use of univariate charts, nevertheless, there are some disadvantages. The main problem is how to interpret the out-ofcontrol signal of a multivariate chart. For example, in the case of control charts designed to monitor the mean vector, the chart signals showing that it must...

Mukherjee and Chakraborti 1 proposed a single distribution-free (nonparametric) Shewhart-type chart based on the Lepage 2 statistic for simultaneously monitoring both the location and the scale parameters of a continuous distribution when both of these parameters are unknown. In the present work, we consider a single distribution-free CUSUM chart, based on the Lepage 2 statistic, referred to as...

In reality, the process observations are away from the assumption that are normal distributed. The observations could be skew distributions which should use an asymmetric chart rather than symmetric chart. Consequently, this research aim to study the robustness of the asymmetric Tukey’s control chart for skew and non-skew distributions as Lognormal and Laplace distributions. Furthermore, the pe...

Decision procedures for monitoring industrial processes can be based on application of control charts. The commonly used p-chart and np-chart are unsatisfactory for monitoring high-quality processes with a low fraction nonconforming. To overcome this difficulty, one may develop models based on the number of items inspected until r (P 1) nonconforming items are observed. The cumulative count con...