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Monitoring and change-point estimation for spline-modeled non-linear profiles in phase II

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  • Zahra Hadidoust
  • Yaser Samimi
  • Hamid Shahriari

Abstract

In some applications of statistical quality control, quality of a process or a product is best characterized by a functional relationship between a response variable and one or more explanatory variables. This relationship is referred to as a profile. In certain cases, the quality of a process or a product is better described by a non-linear profile which does not follow a specific parametric model. In these circumstances, nonparametric approaches with greater flexibility in modeling the complicated profiles are adopted. In this research, the spline smoothing method is used to model a complicated non-linear profile and the Hotelling T -super-2 control chart based on the spline coefficients is used to monitor the process. After receiving an out-of-control signal, a maximum likelihood estimator is employed for change point estimation. The simulation studies, which include both global and local shifts, provide appropriate evaluation of the performance of the proposed estimation and monitoring procedure. The results indicate that the proposed method detects large global shifts while it is very sensitive in detecting local shifts.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:japsta:v:42:y:2015:i:12:p:2520-2530
    DOI: 10.1080/02664763.2015.1043864
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    References listed on IDEAS

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    1. 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.
    2. Kamran Paynabar & Jionghua Jin, 2011. "Characterization of non-linear profiles variations using mixed-effect models and wavelets," IISE Transactions, Taylor & Francis Journals, vol. 43(4), pages 275-290.
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    Cited by:

    1. Hua Xin & Wan-Ju Hsieh & Yuhlong Lio & Tzong-Ru Tsai, 2020. "Nonlinear Profile Monitoring Using Spline Functions," Mathematics, MDPI, vol. 8(9), pages 1-20, September.

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