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A generalized likelihood ratio test for monitoring profile data

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Listed:
  • Yang Liu
  • JunJia Zhu
  • Dennis K. J. Lin

Abstract

Profile data emerges when the quality of a product or process is characterized by a functional relationship among (input and output) variables. In this paper, we focus on the case where each profile has one response variable Y, one explanatory variable x, and the functional relationship between these two variables can be rather arbitrary. The basic concept can be applied to a much wider case, however. We propose a general method based on the Generalized Likelihood Ratio Test (GLRT) for monitoring of profile data. The proposed method uses nonparametric regression to estimate the on-line profiles and thus does not require any functional form for the profiles. Both Shewhart-type and EWMA-type control charts are considered. The average run length (ARL) performance of the proposed method is studied. It is shown that the proposed GLRT-based control chart can efficiently detect both location and dispersion shifts of the on-line profiles from the baseline profile. An upper control limit (UCL) corresponding to a desired in-control ARL value is constructed.

Suggested Citation

  • Yang Liu & JunJia Zhu & Dennis K. J. Lin, 2021. "A generalized likelihood ratio test for monitoring profile data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 48(8), pages 1402-1415, June.
    • Handle: RePEc:taf:japsta:v:48:y:2021:i:8:p:1402-1415
    DOI: 10.1080/02664763.2021.1880555
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