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The performance of smoothed location model with PCA+Indicator MCA and PCA+Adjusted MCA


Ngu, Penny Ai Huong and Hamid, Hashibah and Aziz, Nazrina (2016) The performance of smoothed location model with PCA+Indicator MCA and PCA+Adjusted MCA. AIP Conference Proceedings, 1782. 050008. ISSN 0094-243X

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Abstract

Smoothed location model (SLM) is one of the discriminant analysis that can be used to deal with mixtures of continuous and binary variables simultaneously. However, SLM facing the problem in estimating parameters when the there is a large number of binary variables considered in the study. Thus, two variable extraction techniques, principal component analysis (PCA) and multiple correspondence analysis (MCA) are conducted together with SLM in order to solve the problems of many empty cells and parameters estimation. Simulation results showed that SLM along with PCA+Adjusted MCA performed better than SLM with PCA+ Indicator MCA even when the number of extracted binary is large.

Item Type: Article
Additional Information: Presented at 4th International Conference On Quantitative Sciences And Its Applications (ICOQSIA 2016), 16–18 August 2016, Bangi, Selangor, Malaysia ISBN: 978-0-7354-1444-0
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: School of Quantitative Sciences
Depositing User: Dr. Hashibah Hamid
Date Deposited: 16 Apr 2017 06:01
Last Modified: 16 Apr 2017 06:01
URI: http://repo.uum.edu.my/id/eprint/21577

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