摘要： ► We propose a framework for nonparametric profile monitoring. ► The correlation within the profile is incorporated. ► The Support Vector Regression model and block bootstrap sampling are employed. ► The framework is illustrated on an AIDS data set and shown effective. 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. 出版者： Elsevier Ltd 出版日期： 2012-02 出處： Journal of process control, 2012-02, Vol.22 (2), p.397-403 版權： 2011 Elsevier Ltd 識別號： ISSN: 0959-1524 識別號： EISSN: 1873-2771 識別號： DOI: 10.1016/j.jprocont.2011.12.009
