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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Tsui-Chiao Chao |
| Copyright Year | 2010 |
| Description | Author affiliation: Department of Industrial Engineering and Management, Yuan Ze University, Chungli, Taoyuan, Taiwan (Tsui-Chiao Chao) |
| Abstract | The selection of a suitable set of variables is always a key concern for successful regression modeling. Though stepwise regression with setting threshold of p-value as the stopping criterion is one of the most popular techniques for variable selection, it usually suffers the over-fitting and sample-dependent problems, especially when the number of data points is small relative to the number of variables. To cope with the over-fitting problem, the data set is separated into two parts: training and testing. In addition to evaluate the p-values, the comparison of prediction errors with respect to various models conducted on testing data is also embedded in the stopping criterion. Furthermore, to cope with the sample-dependent problem, the resampling technique is applied to compare the performance of various sets of variables selected in individual resampling runs. A set of semiconductor E-test data collected from a foundry is used to demonstrate the advantages of the proposed method against stepwise regressions. |
| Starting Page | 1 |
| Ending Page | 6 |
| File Size | 365742 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424472956 |
| e-ISBN | 9781424472970 |
| DOI | 10.1109/ICCIE.2010.5668299 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-07-25 |
| Publisher Place | Japan |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Training Stepwise regression Over-fitting Input variables Training data Metrology Data models Manufacturing Sample-dependent Resampling Testing Variable selection |
| Content Type | Text |
| Resource Type | Article |
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