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    政大機構典藏 > 商學院 > 統計學系 > 期刊論文 >  Item 140.119/71323


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    题名: Nonparametric Profile Monitoring in Multi-dimensional Data Spaces.
    作者: Hung,Ying-Chao;Tsai,Wen-Chi;Yang,Su-Fen;Chuang,Shih-Chung;Tseng,Yi-Kuan
    贡献者: 統計系
    关键词: Nonparametric profile monitoring;Support Vector Regression;Block bootstrap;Confidence region
    日期: 2012-02
    上传时间: 2014-11-11 11:02:58 (UTC+8)
    摘要: 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.
    關聯: Journal of Process Control,22(2),397-403
    数据类型: article
    DOI 連結: http://dx.doi.org/http://dx.doi.org/10.1016/j.jprocont.2011.12.009
    DOI: 10.1016/j.jprocont.2011.12.009
    显示于类别:[統計學系] 期刊論文

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