Model selection for autocorrelated residual control charts

Jeh-Nan Pan, Chia Tse His, Sheau Chiann Chen

Research output: Contribution to journalArticlepeer-review

Abstract

When the process data are correlated, one should first select a suitable model to fit the data and then use the traditional control charts to monitor the change of residual. The effectiveness of using residual control charts depend crucially on the appropriateness of the model selected. In this paper, several order-selection criteria including AICC (bias-corrected Akaike's information criterion), BIC (Akaike's Bayesian modification of AIC), and WIC (Weighted average Information Criterion) are used to select the model order when the process data follows a time series model like ARMA (Auto-Regressive and Moving Average). The performances of these criteria are further demonstrated by simulation under different sample size as well as a numerical example. The results show that the performances of model selection using WIC are more robust than other criteria such as AIC, AICC, BIC and SIC.

Original languageEnglish
Pages (from-to)245-260
Number of pages16
JournalJournal of Quality
Volume16
Issue number4
Publication statusPublished - 2009 Oct 5

All Science Journal Classification (ASJC) codes

  • Safety, Risk, Reliability and Quality
  • Management Science and Operations Research

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