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

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Abstract

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.

Item Type:	Article
Subjects:	Q Science > QA Mathematics
Divisions:	School of Computing
Depositing User:	Mr. Jastini Mohd Jamil
Date Deposited:	21 Dec 2015 03:20
Last Modified:	27 Apr 2016 07:05
URI:	https://repo.uum.edu.my/id/eprint/16642

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