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

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

18 Scopus Citations
View graph of relations



Original languageEnglish
Pages (from-to)70-78
Journal / PublicationTechnometrics
Issue number1
Online published31 Jul 2017
Publication statusPublished - 2018


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.

Research Area(s)

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