Pairwise Estimation of Multivariate Gaussian Process Models With Replicated Observations: Application to Multivariate Profile Monitoring

Yongxiang Li, Qiang Zhou, Xiaohu Huang, Li Zeng

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

Profile monitoring is often conducted when the product quality is characterized by profiles. Although existing methods almost exclusively deal with univariate profiles, observations of multivariate profile data are increasingly encountered in practice. These data are seldom analyzed in the area of statistical process control due to lack of effective modeling tools. In this article, we propose to analyze them using the multivariate Gaussian process model, which offers a natural way to accommodate both within-profile and between-profile correlations. To mitigate the prohibitively high computation in building such models, a pairwise estimation strategy is adopted. Asymptotic normality of the parameter estimates from this approach has been established. Comprehensive simulation studies are conducted. In the case study, the method has been demonstrated using transmittance profiles from low-emittance glass. Supplementary materials for this article are available online.

Original languageEnglish (US)
Pages (from-to)70-78
Number of pages9
JournalTechnometrics
Volume60
Issue number1
DOIs
StatePublished - Jan 2 2018

Keywords

  • Asymptotic properties
  • Composite likelihood
  • Multivariate Gaussian process
  • Pairwise estimation
  • Statistical process control

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Applied Mathematics

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