Nonparametric profile monitoring in multi-dimensional data spaces
Journal
Journal of Process Control
Journal Volume
22
Journal Issue
2
Pages
397-403
Date Issued
2012
Author(s)
Abstract
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. © 2011 Elsevier Ltd. All rights reserved.
Subjects
Block bootstrap; Confidence region; Nonparametric profile monitoring; Support Vector Regression
SDGs
Other Subjects
Block bootstrap; Computationally efficient; Confidence region; Explanatory variables; Multidimensional data; Non-parametric; Profile monitoring; Smoothing techniques; Structural assumption; Support Vector Regression; Support vector regressions; Statistical process control; Statistical methods
Type
journal article