| dc.contributor | 統計系 | en_US |
| dc.creator (作者) | Hung,Ying-Chao;Tsai,Wen-Chi;Yang,Su-Fen;Chuang,Shih-Chung;Tseng,Yi-Kuan | en_US |
| dc.date (日期) | 2012-02 | en_US |
| dc.date.accessioned | 11-Nov-2014 11:02:58 (UTC+8) | - |
| dc.date.available | 11-Nov-2014 11:02:58 (UTC+8) | - |
| dc.date.issued (上傳時間) | 11-Nov-2014 11:02:58 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/71323 | - |
| dc.description.abstract (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. | en_US |
| dc.format.extent | 444575 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.language.iso | en_US | - |
| dc.relation (關聯) | Journal of Process Control,22(2),397-403 | en_US |
| dc.subject (關鍵詞) | Nonparametric profile monitoring; Support Vector Regression; Block bootstrap; Confidence region | en_US |
| dc.title (題名) | Nonparametric Profile Monitoring in Multi-dimensional Data Spaces. | en_US |
| dc.type (資料類型) | article | en |
| dc.identifier.doi (DOI) | 10.1016/j.jprocont.2011.12.009 | en_US |
| dc.doi.uri (DOI) | http://dx.doi.org/http://dx.doi.org/10.1016/j.jprocont.2011.12.009 | en_US |
