Nonparametric profile monitoring in multi-dimensional data spaces

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.

► 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.