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https://ah.lib.nccu.edu.tw/handle/140.119/18177
題名: | Kernel-based discriminant techniques for educational placement | 作者: | 林妙香;張源俊 Lin,Miao-hsiang ;Huang ,Su-yun ;Chang,Yuan-chin |
日期: | Jan-2004 | 上傳時間: | 19-Dec-2008 | 摘要: | This article considers the problem of educational placement. Several discriminant techniques are applied to a data set from a survey project of science ability. A profile vector for each student consists of five science-educational indictors. The students are intended to be placed into three reference groups: advanced, regular, and remedial. Various discriminant techniques, including Fisher’s discriminant analysis and kernel-based nonparametric discriminant analysis, are compared. The evaluation work is based on the leaving-one-out misclassification score. Results from the five school data sets and 500 bootstrap samples reveal that the kernel-based nonparametric approach with bandwidth selected by cross validation performs reasonably well. The authors regard kernel-based nonparametric procedures as desirable competitors to Fisher’s discriminant rule for handling problems of educational placement. | 關聯: | Journal of Educational and Behavioral Statistics, 29, 219-241 | 資料類型: | article |
Appears in Collections: | 期刊論文 |
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File | Description | Size | Format | |
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219-240.pdf | 198.89 kB | Adobe PDF2 | View/Open |
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