Nonparametric profile monitoring in multi-dimensional data spaces

Ying Chao Hung, Wen Chi Tsai, Su Fen Yang, Shih Chung Chuang, Yi Kuan Tseng

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


Profile monitoring has received increasingly attention in a wide range of applications in statistical process control (SPC). In this work, we propose a framework for monitoring nonparametric profiles in multi-dimensional data spaces. The framework has the following important features: (i) a flexible and computationally efficient smoothing technique, called Support Vector Regression, is employed to describe the relationship between the response variable and the explanatory variables; (ii) the usual structural assumptions on the residuals are not required; and (iii) the dependence structure for the within-profile observations is appropriately accommodated. Finally, real AIDS data collected from hospitals in Taiwan are used to illustrate and evaluate our proposed framework.

Original languageEnglish
Pages (from-to)397-403
Number of pages7
JournalJournal of Process Control
Issue number2
StatePublished - Feb 2012


  • Block bootstrap
  • Confidence region
  • Nonparametric profile monitoring
  • Support Vector Regression


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