KNUSTSpace >
Research Articles >
College of Science >

Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/11381

Title: Asymptotic performance of the quadratic discriminant function to skewed training samples
Authors: Adebanji, Atinuke
Asamoah‑Boaheng, Michael
Osei-Tutu, Olivia
Keywords: Group centroid separator
Lognormal distribution
Error rates
Coefficient of Variation
Issue Date: 2016
Publisher: SpringerPlus
Citation: SpringerPlus (2016), 5:1530; DOI 10.1186/s40064-016-3204-3
Abstract: This study investigates the asymptotic performance of the quadratic discriminant function (QDF) under skewed training samples. The main objective of this study is to evaluate the performance of the QDF under skewed distribution considering different sample size ratios, varying the group centroid separators and the number of variables. Three populations ( i , i = 1, 2, 3) with increasing group centroid separator function were considered. A multivariate normal distributed data was simulated with MatLab R2009a. There was an increase in the average error rates of the sample size ratios 1:2:2 and 1:2:3 as the total sample size increased asymptotically in the skewed distribution when the centroid separator increased from 1 to 3. The QDF under the skewed distribution performed better for the sample size ratio 1:1:1 as compared to the other sampling ratios and under centroid separator ( = 5).
Description: An article published by SpringerPlus (2016), 5:1530; DOI 10.1186/s40064-016-3204-3
URI: http://hdl.handle.net/123456789/11381
Appears in Collections:College of Science

Files in This Item:

File Description SizeFormat
Michael and Adebanji.pdf1.92 MBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.


Valid XHTML 1.0! DSpace Software Copyright © 2002-2010  Duraspace - Feedback