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Constructing partial least squares model in the presence of missing data

Virtually all methods of data analysis are plagued by problems with missing data, and partial least squares are no exception.This work contributes to knowledge by critically examining ways of handling missing data in the estimation of Partial Least Squares Models. The experiments are performed using...

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Main Authors: Mohd Jamil, Jastini, Mohd Shaharanee, Izwan Nizal
Format: Article
Published: American Scientific Publishers 2015
Subjects:
QA Mathematics
Online Access:http://repo.uum.edu.my/16642/
http://doi.org/10.1166/asl.2015.6120
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spelling my.uum.repo.166422016-04-27T07:05:41Z http://repo.uum.edu.my/16642/ Constructing partial least squares model in the presence of missing data Mohd Jamil, Jastini Mohd Shaharanee, Izwan Nizal QA Mathematics Virtually all methods of data analysis are plagued by problems with missing data, and partial least squares are no exception.This work contributes to knowledge by critically examining ways of handling missing data in the estimation of Partial Least Squares Models. The experiments are performed using real world customer satisfaction dataset.The results indicate that Multiple Imputation performs better than the other methods for all percentages of missing data. Another unique contribution is found when comparing the results before and after the Neural Network post-processing procedure.This improvement in accuracy is resulted from the neural network’s ability to derive meaning from the imputed data set found by the statistical methods. American Scientific Publishers 2015 Article PeerReviewed Mohd Jamil, Jastini and Mohd Shaharanee, Izwan Nizal (2015) Constructing partial least squares model in the presence of missing data. Advanced Science Letters, 21 (6). pp. 1704-1707. ISSN 1936-6612 http://doi.org/10.1166/asl.2015.6120 doi:10.1166/asl.2015.6120
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/
topic QA Mathematics
spellingShingle QA Mathematics
Mohd Jamil, Jastini
Mohd Shaharanee, Izwan Nizal
Constructing partial least squares model in the presence of missing data
description Virtually all methods of data analysis are plagued by problems with missing data, and partial least squares are no exception.This work contributes to knowledge by critically examining ways of handling missing data in the estimation of Partial Least Squares Models. The experiments are performed using real world customer satisfaction dataset.The results indicate that Multiple Imputation performs better than the other methods for all percentages of missing data. Another unique contribution is found when comparing the results before and after the Neural Network post-processing procedure.This improvement in accuracy is resulted from the neural network’s ability to derive meaning from the imputed data set found by the statistical methods.
format Article
author Mohd Jamil, Jastini
Mohd Shaharanee, Izwan Nizal
author_facet Mohd Jamil, Jastini
Mohd Shaharanee, Izwan Nizal
author_sort Mohd Jamil, Jastini
title Constructing partial least squares model in the presence of missing data
title_short Constructing partial least squares model in the presence of missing data
title_full Constructing partial least squares model in the presence of missing data
title_fullStr Constructing partial least squares model in the presence of missing data
title_full_unstemmed Constructing partial least squares model in the presence of missing data
title_sort constructing partial least squares model in the presence of missing data
publisher American Scientific Publishers
publishDate 2015
url http://repo.uum.edu.my/16642/
http://doi.org/10.1166/asl.2015.6120
_version_ 1644282025659596800
score 13.15806

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