Model recognition by using Principal Component Analysis (PCA) approach
In this paper, an alternative model recognition method is proposed by using Principal Component Analysis (PCA). This alternative approach is used to choose the optimum model for fitting the index of real compensation per hour (Y) and labor productivity per hour (X) in the business sector of the U.S....
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| Format: | Article |
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Faculty of Science, Chiang Mai University
2014
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| _version_ | 1825948469988163584 |
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| author | Ud-Doulah, Md. Siraj Rana, Md. Sohel Midi, Habshah |
| author_facet | Ud-Doulah, Md. Siraj Rana, Md. Sohel Midi, Habshah |
| author_sort | Ud-Doulah, Md. Siraj |
| collection | UPM |
| description | In this paper, an alternative model recognition method is proposed by using Principal Component Analysis (PCA). This alternative approach is used to choose the optimum model for fitting the index of real compensation per hour (Y) and labor productivity per hour (X) in the business sector of the U.S. economy for the period 1960–1991. Comparison is then made with the existing methods such as ranks of the, Adjusted (), Akaike Information Criterion (AIC) and Schwarz’s Information Criterion (SIC) values. The empirical evidence shows that the proposed method has the same ability to choose the best fitted models. The main attraction of this method is that it can be applied to all types of data scale; however, the existing methods not work for all types of data scale. Additionally, the proposed method has a clear edge over its rival because the PCA uses actual observations. Hence, we suggest to use the proposed method instead of the existing methods in determining the best fitted model. |
| first_indexed | 2024-03-06T08:29:18Z |
| format | Article |
| id | upm.eprints-34541 |
| institution | Universiti Putra Malaysia |
| last_indexed | 2024-03-06T08:29:18Z |
| publishDate | 2014 |
| publisher | Faculty of Science, Chiang Mai University |
| record_format | dspace |
| spelling | upm.eprints-345412015-12-16T01:04:32Z http://psasir.upm.edu.my/id/eprint/34541/ Model recognition by using Principal Component Analysis (PCA) approach Ud-Doulah, Md. Siraj Rana, Md. Sohel Midi, Habshah In this paper, an alternative model recognition method is proposed by using Principal Component Analysis (PCA). This alternative approach is used to choose the optimum model for fitting the index of real compensation per hour (Y) and labor productivity per hour (X) in the business sector of the U.S. economy for the period 1960–1991. Comparison is then made with the existing methods such as ranks of the, Adjusted (), Akaike Information Criterion (AIC) and Schwarz’s Information Criterion (SIC) values. The empirical evidence shows that the proposed method has the same ability to choose the best fitted models. The main attraction of this method is that it can be applied to all types of data scale; however, the existing methods not work for all types of data scale. Additionally, the proposed method has a clear edge over its rival because the PCA uses actual observations. Hence, we suggest to use the proposed method instead of the existing methods in determining the best fitted model. Faculty of Science, Chiang Mai University 2014-01 Article PeerReviewed Ud-Doulah, Md. Siraj and Rana, Md. Sohel and Midi, Habshah (2014) Model recognition by using Principal Component Analysis (PCA) approach. Chiang Mai Journal of Science, 41 (1). pp. 224-230. ISSN 0125-2526; ESSN: 2465-3845 http://it.science.cmu.ac.th/ejournal/journalDetail.php?journal_id=4587 |
| spellingShingle | Ud-Doulah, Md. Siraj Rana, Md. Sohel Midi, Habshah Model recognition by using Principal Component Analysis (PCA) approach |
| title | Model recognition by using Principal Component Analysis (PCA) approach |
| title_full | Model recognition by using Principal Component Analysis (PCA) approach |
| title_fullStr | Model recognition by using Principal Component Analysis (PCA) approach |
| title_full_unstemmed | Model recognition by using Principal Component Analysis (PCA) approach |
| title_short | Model recognition by using Principal Component Analysis (PCA) approach |
| title_sort | model recognition by using principal component analysis pca approach |
| work_keys_str_mv | AT uddoulahmdsiraj modelrecognitionbyusingprincipalcomponentanalysispcaapproach AT ranamdsohel modelrecognitionbyusingprincipalcomponentanalysispcaapproach AT midihabshah modelrecognitionbyusingprincipalcomponentanalysispcaapproach |