Multivariate binomial/multinomial control chart

Abstract

This article considers statistical process control for multivariate categorical processes. In particular, there is a focus on multivariate binomial and multivariate multinomial processes. More and more real applications involve categorical quality characteristics, which cannot be measured on a continuous scale. These characteristic factors usually correlate with each other, indicating a need for multivariate charting techniques. However, there is a scarcity of research on monitoring multivariate categorical data, and most existing methods lack robustness for some deficiencies. This article reports the use of log-linear models for characterizing the relationship among categorical factors that are adapted into a framework of multivariate binomial and multivariate multinomial distributions. A Phase II control chart is proposed that is robust in efficiently detecting various shifts, especially those in interaction effects representing the dependence among factors. Numerical simulations and a real data example demonstrate the effectiveness of the chart.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 64 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.