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Online monitoring of auto correlated linear profiles via mixed model

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  • Paria Soleimani
  • Ali Narvand
  • Sadigh Raissi

Abstract

In statistical quality control a profile can be characterised by a given mathematical function between a quality characteristic and one or more explanatory process variables. Most existing control charts in the literature have been proposed for profile monitoring with the independence assumption of the observation within profiles. However in certain situation, this assumption can be violated. The present study focused on phase II of a linear profile monitoring and extends Jensen et al. (2008)'s work in applying linear mixed models on the presence of autocorrelation within profiles. Three methods namely Hotteling T², multivariate exponential weighted moving average (MEWMA) control chart and multivariate cumulative sum (MCUSUM) control chart are discussed and their performances are compared in term of average run length (ARL). These techniques are illustrated with a real data set taken from an agriculture field.

Suggested Citation

  • Paria Soleimani & Ali Narvand & Sadigh Raissi, 2013. "Online monitoring of auto correlated linear profiles via mixed model," International Journal of Manufacturing Technology and Management, Inderscience Enterprises Ltd, vol. 27(4/5/6), pages 238-250.
    • Handle: RePEc:ids:ijmtma:v:27:y:2013:i:4/5/6:p:238-250
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    Cited by:

    1. Wenhui Liu & Zhonghua Li & Zhaojun Wang, 2022. "Monitoring of Linear Profiles Using Linear Mixed Model in the Presence of Measurement Errors," Mathematics, MDPI, vol. 10(24), pages 1-17, December.
    2. Zahra Hadidoust & Yaser Samimi & Hamid Shahriari, 2015. "Monitoring and change-point estimation for spline-modeled non-linear profiles in phase II," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(12), pages 2520-2530, December.

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