Smoothed location model is a discriminant analysis which can be used to handle the data involving... more Smoothed location model is a discriminant analysis which can be used to handle the data involving mixtures of continuous and binary variables simultaneously. This model is introduced to handle the problem of some empty cells due to the increasing of binary variables. However, smoothed location model is infeasible if involve large number of binary variables. Therefore, the combination of two variable extraction approaches, principal component analysis and multiple correspondence analysis are carried out before the construction of smoothed location model in order to extract large number of measured variables in the study. In fact, there are four types of multiple correspondence analysis but only Burt matrix multiple correspondence analysis had been applied in the latest investigation. Thus, this study aims to examine and compare principal component analysis with four types of multiple correspondence analysis and hope to have better results for data with large number of mixed variables. The proposed model is...
Non-parametric smoothed location model is another powerful approach which can be used to discrimi... more Non-parametric smoothed location model is another powerful approach which can be used to discriminate the objects that contain both continuous and binary variables.However, the smoothed location model is infeasible in estimating parameters when a large number of binary variables involved in the study.To handle this issue, the combination of two variable extraction techniques namely principal component analysis (PCA) and multiple correspondence analysis (MCA) are carried out before the construction of the smoothed location model. In fact, there are four types of MCA but only Indicator MCA and joint correspondence analysis (JCA) will be discussed in this article.Thus, the performance of the smoothed location model together with combination of PCA and two types of MCA, i.e. Indicator MCA and JCA, will be compared and evaluated.The overall results from simulation study show that the smoothed location model performed better when the binary extraction is done by JCA rather than the Indica...
The issue of classifying objects into groups when the measured variables are mixtures of continuo... more The issue of classifying objects into groups when the measured variables are mixtures of continuous and binary variables has attracted the attention of statisticians. Among the discriminant methods in classification, Smoothed Location Model (SLM) is used to handle data that contains both continuous and binary variables simultaneously. However, this model is infeasible if the data is having a large number of binary variables. The presence of huge binary variables will create numerous multinomial cells that will later cause the occurrence of large number of empty cells. Past studies have shown that the occurrence of many empty cells affected the performance of the constructed smoothed location model. In order to overcome the problem of many empty cells due to large number of measured variables (mainly binary), this study proposes four new SLMs by combining the existing SLM with Principal Component Analysis (PCA) and four types of Multiple Correspondence Analysis (MCA). PCA is used to ...
Non-parametric smoothed location model is another powerful approach which can be used to discrimi... more Non-parametric smoothed location model is another powerful approach which can be used to discriminate the objects that contain both continuous and binary variables. However, the smoothed location model is infeasible in estimating parameters when a large number of binary variables involved in the study. To handle this issue, the combination of two variable extraction techniques namely principal component analysis (PCA) and multiple correspondence analysis (MCA) are carried out before the construction of the smoothed location model. In fact, there are four types of MCA but only Indicator MCA and joint correspondence analysis (JCA) will be discussed in this article. Thus, the performance of the smoothed location model together with combination of PCA and two types of MCA, i.e. Indicator MCA and JCA, will be compared and evaluated. The overall results from simulation study show that the smoothed location model performed better when the binary extraction is done by JCA rather than the Indicator MCA in terms of misclassification rate and computational efficiency.
Smoothed location model is a discriminant analysis which can be used to handle the data involving... more Smoothed location model is a discriminant analysis which can be used to handle the data involving mixtures of continuous and binary variables simultaneously. This model is introduced to handle the problem of some empty cells due to the increasing of binary variables. However, smoothed location model is infeasible if involve large number of binary variables. Therefore, the combination of two variable extraction approaches, principal component analysis and multiple correspondence analysis are carried out before the construction of smoothed location model in order to extract large number of measured variables in the study. In fact, there are four types of multiple correspondence analysis but only Burt matrix multiple correspondence analysis had been applied in the latest investigation. Thus, this study aims to examine and compare principal component analysis with four types of multiple correspondence analysis and hope to have better results for data with large number of mixed variables. The proposed model is...
Non-parametric smoothed location model is another powerful approach which can be used to discrimi... more Non-parametric smoothed location model is another powerful approach which can be used to discriminate the objects that contain both continuous and binary variables.However, the smoothed location model is infeasible in estimating parameters when a large number of binary variables involved in the study.To handle this issue, the combination of two variable extraction techniques namely principal component analysis (PCA) and multiple correspondence analysis (MCA) are carried out before the construction of the smoothed location model. In fact, there are four types of MCA but only Indicator MCA and joint correspondence analysis (JCA) will be discussed in this article.Thus, the performance of the smoothed location model together with combination of PCA and two types of MCA, i.e. Indicator MCA and JCA, will be compared and evaluated.The overall results from simulation study show that the smoothed location model performed better when the binary extraction is done by JCA rather than the Indica...
The issue of classifying objects into groups when the measured variables are mixtures of continuo... more The issue of classifying objects into groups when the measured variables are mixtures of continuous and binary variables has attracted the attention of statisticians. Among the discriminant methods in classification, Smoothed Location Model (SLM) is used to handle data that contains both continuous and binary variables simultaneously. However, this model is infeasible if the data is having a large number of binary variables. The presence of huge binary variables will create numerous multinomial cells that will later cause the occurrence of large number of empty cells. Past studies have shown that the occurrence of many empty cells affected the performance of the constructed smoothed location model. In order to overcome the problem of many empty cells due to large number of measured variables (mainly binary), this study proposes four new SLMs by combining the existing SLM with Principal Component Analysis (PCA) and four types of Multiple Correspondence Analysis (MCA). PCA is used to ...
Non-parametric smoothed location model is another powerful approach which can be used to discrimi... more Non-parametric smoothed location model is another powerful approach which can be used to discriminate the objects that contain both continuous and binary variables. However, the smoothed location model is infeasible in estimating parameters when a large number of binary variables involved in the study. To handle this issue, the combination of two variable extraction techniques namely principal component analysis (PCA) and multiple correspondence analysis (MCA) are carried out before the construction of the smoothed location model. In fact, there are four types of MCA but only Indicator MCA and joint correspondence analysis (JCA) will be discussed in this article. Thus, the performance of the smoothed location model together with combination of PCA and two types of MCA, i.e. Indicator MCA and JCA, will be compared and evaluated. The overall results from simulation study show that the smoothed location model performed better when the binary extraction is done by JCA rather than the Indicator MCA in terms of misclassification rate and computational efficiency.
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