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    政大機構典藏 > 商學院 > 統計學系 > 期刊論文 >  Item 140.119/18177
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/18177

    Title: Kernel-based discriminant techniques for educational placement
    Authors: 林妙香;張源俊
    Lin,Miao-hsiang;Huang ,Su-yun;Chang,Yuan-chin
    Date: 2004-01
    Issue Date: 2008-12-19 14:53:08 (UTC+8)
    Abstract: 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.
    Relation: Journal of Educational and Behavioral Statistics, 29, 219-241
    Data Type: article
    Appears in Collections:[統計學系] 期刊論文

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