A new framework of smoothed location model with multiple correspondence analysis
The implication of a considering large binary variables into the smoothed location model will create too many multinomial cells or lead to high multinomial cells and more worrying is that it will cause most of them are empty. We refer this situation as large sparsity problem. When large sparsity of...
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| Format: | Conference or Workshop Item |
| Published: |
2016
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| Subjects: | |
| Online Access: | http://repo.uum.edu.my/21576/ http://doi.org/10.1007/978-981-10-2772-7_12 |
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| Institution: | Universiti Utara Malaysia |
| Summary: | The implication of a considering large binary variables into the smoothed location model will create too many multinomial cells or lead to high multinomial cells and more worrying is that it will cause most of them are empty. We refer this situation as large sparsity problem. When large sparsity of multinomial cells occurs, the smoothed estimators of location model will be greatly biased, hence creating frustrating performance. At worst, the classification rules cannot be constructed. This issue has attracted this paper to further investigate and propose a new approach of the smoothed location model when facing with large sparsity problem. |
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