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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my.uum.repo.215762017-04-16T08:58:03Z http://repo.uum.edu.my/21576/ A new framework of smoothed location model with multiple correspondence analysis Hamid, Hashibah QA Mathematics 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. 2016 Conference or Workshop Item PeerReviewed Hamid, Hashibah (2016) A new framework of smoothed location model with multiple correspondence analysis. In: International Conference on Computing, Mathematics and Statistics (iCMS 2015), November 2015, Langkawi. http://doi.org/10.1007/978-981-10-2772-7_12 doi:10.1007/978-981-10-2772-7_12 |
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QA Mathematics Hamid, Hashibah A new framework of smoothed location model with multiple correspondence analysis |
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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. |
| format |
Conference or Workshop Item |
| author |
Hamid, Hashibah |
| author_facet |
Hamid, Hashibah |
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Hamid, Hashibah |
| title |
A new framework of smoothed location model with multiple correspondence analysis |
| title_short |
A new framework of smoothed location model with multiple correspondence analysis |
| title_full |
A new framework of smoothed location model with multiple correspondence analysis |
| title_fullStr |
A new framework of smoothed location model with multiple correspondence analysis |
| title_full_unstemmed |
A new framework of smoothed location model with multiple correspondence analysis |
| title_sort |
new framework of smoothed location model with multiple correspondence analysis |
| publishDate |
2016 |
| url |
http://repo.uum.edu.my/21576/ http://doi.org/10.1007/978-981-10-2772-7_12 |
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