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...
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| Format: | Article |
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IP Publishing LLC
2016
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| Summary: | 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. |
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