Control charts based on randomized quantile residuals

In practice, quality characteristics do not always follow a normal distribution, and quality control processes sometimes generate non‐normal response outcomes, including continuous non‐normal data and discrete count data. Thus, achieving better results in such situations requires a new control chart...

Full description

Saved in:
Bibliographic Details
Published inApplied stochastic models in business and industry Vol. 36; no. 4; pp. 716 - 729
Main Authors Park, Kayoung, Jung, Dongmin, Kim, Jong‐Min
Format Journal Article
LanguageEnglish
Published Bognor Regis Wiley Subscription Services, Inc 01.07.2020
Subjects
Computer simulation
control chart
Control charts
deviance residual
Normal distribution
Quality control
Randomization
randomized quantile residual
Regression models
Statistical analysis
statistical processes control
Online AccessGet full text

Cover

More Information
Summary:In practice, quality characteristics do not always follow a normal distribution, and quality control processes sometimes generate non‐normal response outcomes, including continuous non‐normal data and discrete count data. Thus, achieving better results in such situations requires a new control chart derived from various types of response variables. This study proposes a procedure for monitoring response variables that uses control charts based on randomized quantile residuals obtained from a fitted regression model. Simulation studies demonstrate the performance of the proposed control charts under various situations. We illustrate the procedure using two real‐data examples, based on normal and negative binomial regression models, respectively. The simulation and real‐data results support our proposed procedure.
ISSN:1524-1904
1526-4025
DOI:10.1002/asmb.2527