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Using principal component analysis to extract mixed variables for smoothed location model

This study is conducted to test the appropriateness of variables extraction technique called principal component analysis to keep adequate number of variables for construction of the smoothed location model when the measured variables are mixed and large, particularly the binary.The strategy of perf...

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Main Authors: Hamid, Hashibah, Mahat, Nor Idayu
Format: Article
Language:English
Published: Pushpa Publishing House 2013
Subjects:
QA Mathematics
Online Access:http://repo.uum.edu.my/21572/1/FJMS%20%2080%201%202013%2033%2054.pdf
http://repo.uum.edu.my/21572/
http://www.pphmj.com/abstract/7952.htm
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spelling my.uum.repo.215722017-04-16T02:34:38Z http://repo.uum.edu.my/21572/ Using principal component analysis to extract mixed variables for smoothed location model Hamid, Hashibah Mahat, Nor Idayu QA Mathematics This study is conducted to test the appropriateness of variables extraction technique called principal component analysis to keep adequate number of variables for construction of the smoothed location model when the measured variables are mixed and large, particularly the binary.The strategy of performing variables extraction prior to construction of the smoothed location model was tested on some artificial data generated from normal population and also on three real data sets.The criterion for selecting useful components based on unity eigenvalue works well for small and moderate sizes of variables, but troublesome for large number of variables.Results of simulations give evidence that the proposed strategy shows good performance with small value of error rate in classifying future objects through new extracted components for the investigated population.The results also are satisfactory for real data sets.The suggested strategy shows potential to be used as an option for the purpose of classification based on smoothed location model when dealing with large number of mixed variables. Pushpa Publishing House 2013-09 Article PeerReviewed application/pdf en http://repo.uum.edu.my/21572/1/FJMS%20%2080%201%202013%2033%2054.pdf Hamid, Hashibah and Mahat, Nor Idayu (2013) Using principal component analysis to extract mixed variables for smoothed location model. Far East Journal of Mathematical Sciences (FJMS), 80 (1). pp. 33-54. ISSN 0972-0871 http://www.pphmj.com/abstract/7952.htm
institution Universiti Utara Malaysia
building UUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Utara Malaysia
content_source UUM Institutionali Repository
url_provider http://repo.uum.edu.my/
language English
topic QA Mathematics
spellingShingle QA Mathematics
Hamid, Hashibah
Mahat, Nor Idayu
Using principal component analysis to extract mixed variables for smoothed location model
description This study is conducted to test the appropriateness of variables extraction technique called principal component analysis to keep adequate number of variables for construction of the smoothed location model when the measured variables are mixed and large, particularly the binary.The strategy of performing variables extraction prior to construction of the smoothed location model was tested on some artificial data generated from normal population and also on three real data sets.The criterion for selecting useful components based on unity eigenvalue works well for small and moderate sizes of variables, but troublesome for large number of variables.Results of simulations give evidence that the proposed strategy shows good performance with small value of error rate in classifying future objects through new extracted components for the investigated population.The results also are satisfactory for real data sets.The suggested strategy shows potential to be used as an option for the purpose of classification based on smoothed location model when dealing with large number of mixed variables.
format Article
author Hamid, Hashibah
Mahat, Nor Idayu
author_facet Hamid, Hashibah
Mahat, Nor Idayu
author_sort Hamid, Hashibah
title Using principal component analysis to extract mixed variables for smoothed location model
title_short Using principal component analysis to extract mixed variables for smoothed location model
title_full Using principal component analysis to extract mixed variables for smoothed location model
title_fullStr Using principal component analysis to extract mixed variables for smoothed location model
title_full_unstemmed Using principal component analysis to extract mixed variables for smoothed location model
title_sort using principal component analysis to extract mixed variables for smoothed location model
publisher Pushpa Publishing House
publishDate 2013
url http://repo.uum.edu.my/21572/1/FJMS%20%2080%201%202013%2033%2054.pdf
http://repo.uum.edu.my/21572/
http://www.pphmj.com/abstract/7952.htm
_version_ 1644283275961696256
score 13.211869

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