The performance of smoothed location model with PCA+Indicator MCA and PCA+Adjusted MCA
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...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Published: |
IP Publishing LLC
2016
|
| Subjects: |
| _version_ | 1825804585843818496 |
|---|---|
| author | Ngu, Penny Ai Huong Hamid, Hashibah Aziz, Nazrina |
| author_facet | Ngu, Penny Ai Huong Hamid, Hashibah Aziz, Nazrina |
| author_sort | Ngu, Penny Ai Huong |
| collection | UUM |
| description | 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. |
| first_indexed | 2024-07-04T06:17:57Z |
| format | Article |
| id | uum-21577 |
| institution | Universiti Utara Malaysia |
| last_indexed | 2024-07-04T06:17:57Z |
| publishDate | 2016 |
| publisher | IP Publishing LLC |
| record_format | eprints |
| spelling | uum-215772017-04-16T06:01:48Z https://repo.uum.edu.my/id/eprint/21577/ The performance of smoothed location model with PCA+Indicator MCA and PCA+Adjusted MCA Ngu, Penny Ai Huong Hamid, Hashibah Aziz, Nazrina QA75 Electronic computers. Computer science 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. IP Publishing LLC 2016 Article PeerReviewed 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 http://doi.org/10.1063/1.4966098 doi:10.1063/1.4966098 doi:10.1063/1.4966098 |
| spellingShingle | QA75 Electronic computers. Computer science Ngu, Penny Ai Huong Hamid, Hashibah Aziz, Nazrina The performance of smoothed location model with PCA+Indicator MCA and PCA+Adjusted MCA |
| title | The performance of smoothed location model with PCA+Indicator MCA and PCA+Adjusted MCA |
| title_full | The performance of smoothed location model with PCA+Indicator MCA and PCA+Adjusted MCA |
| title_fullStr | The performance of smoothed location model with PCA+Indicator MCA and PCA+Adjusted MCA |
| title_full_unstemmed | The performance of smoothed location model with PCA+Indicator MCA and PCA+Adjusted MCA |
| title_short | The performance of smoothed location model with PCA+Indicator MCA and PCA+Adjusted MCA |
| title_sort | performance of smoothed location model with pca indicator mca and pca adjusted mca |
| topic | QA75 Electronic computers. Computer science |
| work_keys_str_mv | AT ngupennyaihuong theperformanceofsmoothedlocationmodelwithpcaindicatormcaandpcaadjustedmca AT hamidhashibah theperformanceofsmoothedlocationmodelwithpcaindicatormcaandpcaadjustedmca AT aziznazrina theperformanceofsmoothedlocationmodelwithpcaindicatormcaandpcaadjustedmca AT ngupennyaihuong performanceofsmoothedlocationmodelwithpcaindicatormcaandpcaadjustedmca AT hamidhashibah performanceofsmoothedlocationmodelwithpcaindicatormcaandpcaadjustedmca AT aziznazrina performanceofsmoothedlocationmodelwithpcaindicatormcaandpcaadjustedmca |