A nonparametric control chart for monitoring count data mean

Abstract

Abstract Count data monitoring has important applications in many fields. However, most of the existing control charts for monitoring count data are parametric. Parametric control charts can be problematic when the underlying parametric distributional assumption does not hold for the particular application. On the other hand, nonparametric control charts do not require such distributional assumptions, and are more desirable in real‐world situations where the underlying distribution cannot be easily described using a parametric distribution. In this paper, we extend the nonparametric control chart for continuous data monitoring proposed by Li to count data monitoring. To guarantee a desired in‐control performance, we further adopt the bootstrap procedure proposed by Gandy and Kvaløy to help determine the control limit of our proposed control chart. Our simulation studies and real data analysis show that the proposed control chart performs well across a variety of settings, and compares favorably with other existing nonparametric control charts for count data.

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 — 11 citations exist upstream.

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