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政大典藏 > College of Commerce > Department of Statistics > Periodical Articles > Item 140.119/71323

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/71323

Title:	Nonparametric Profile Monitoring in Multi-dimensional Data Spaces.
Authors:	Hung,Ying-Chao;Tsai,Wen-Chi;Yang,Su-Fen;Chuang,Shih-Chung;Tseng,Yi-Kuan
Contributors:	統計系
Keywords:	Nonparametric profile monitoring;Support Vector Regression;Block bootstrap;Confidence region
Date:	2012-02
Issue Date:	2014-11-11 11:02:58 (UTC+8)
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.
Relation:	Journal of Process Control,22(2),397-403
Data Type:	article
DOI link:	http://dx.doi.org/http://dx.doi.org/10.1016/j.jprocont.2011.12.009
DOI:	10.1016/j.jprocont.2011.12.009
Appears in Collections:	[Department of Statistics] Periodical Articles

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