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https://ah.lib.nccu.edu.tw/handle/140.119/61610
題名: | Nonparametric Profile Monitoring in Multi-dimensional Data Spaces | 作者: | 洪英超;楊素芬 Hung,Ying-Chao ; Tsai, Wen-Chi ;Yang, Su-Fen ;Chuang, Shih-Chung ;Tseng, Yi-Kuan |
貢獻者: | 統計系 | 關鍵詞: | Nonparametric profile monitoring;Support Vector Regression;Block bootstrap;Confidence region | 日期: | Feb-2012 | 上傳時間: | 11-Nov-2013 | 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 | 資料類型: | article | DOI: | http://dx.doi.org/http://dx.doi.org/10.1016/j.jprocont.2011.12.009 |
Appears in Collections: | 期刊論文 |
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