| dc.creator (作者) | 林妙香;張源俊 | zh_TW |
| dc.creator (作者) | Lin,Miao-hsiang ;Huang ,Su-yun ;Chang,Yuan-chin | - |
| dc.date (日期) | 2004-01 | en_US |
| dc.date.accessioned | 19-Dec-2008 14:53:08 (UTC+8) | - |
| dc.date.available | 19-Dec-2008 14:53:08 (UTC+8) | - |
| dc.date.issued (上傳時間) | 19-Dec-2008 14:53:08 (UTC+8) | - |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/18177 | - |
| dc.description.abstract (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. | - |
| dc.format | application/ | en_US |
| dc.language | en | en_US |
| dc.language | en-US | en_US |
| dc.language.iso | en_US | - |
| dc.relation (關聯) | Journal of Educational and Behavioral Statistics, 29, 219-241 | en_US |
| dc.title (題名) | Kernel-based discriminant techniques for educational placement | en_US |
| dc.type (資料類型) | article | en |
