Outlier detection using PCA mix basedT2control chart for continuous and categorical data

Outliers presence may lead to misdetection on out-of-control observations in Phase II, therefore, they should be cleaned in Phase I. This paper proposes PCA Mix based T2 chart with Kernel Density control limit for mixed continuous and categorical data. Simulation studies are conducted to evaluate the performance of proposed chart in detecting outliers from clean and contaminated data. The proposed chart has better performance than the benchmark in monitoring clean data. For contaminated data, proposed chart has optimal performance in situation when categorical data are generated from multinomial distribution with balanced parameters. This is confirmed by simulated and real dataset. Compared to the conventional and other robust charts, the proposed chart demonstrated a great performance by success to detect more outlier correctly for the higher percentage of outlier added.