Comparison of parameter estimation methods when multicollinearity and outlier exists / Aida Nurasikin Jamil ...[et al.]
Ordinary Least Squares (OLS) estimator become worse in the presence of multicollinearity and outlier. Here, three methods are suggested to improve the model when multicollinearity and outlier exists, the first one is Jackknife Regression (JR) based on left out method, the second is Ridge Regression...
| Main Authors: | , , , |
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| Format: | Thesis |
| Language: | English |
| Published: |
2019
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| Subjects: | |
| Online Access: | https://ir.uitm.edu.my/id/eprint/32559/1/32559.pdf |
| _version_ | 1796903463416758272 |
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| author | Jamil, Aida Nurasikin Abdul Muluk, Muhammad Fahmi Anuar, Nur Sabrina Abu Bakar, Mohamad Suffian |
| author_facet | Jamil, Aida Nurasikin Abdul Muluk, Muhammad Fahmi Anuar, Nur Sabrina Abu Bakar, Mohamad Suffian |
| author_sort | Jamil, Aida Nurasikin |
| collection | UITM |
| description | Ordinary Least Squares (OLS) estimator become worse in the presence of multicollinearity and outlier. Here, three methods are suggested to improve the model when multicollinearity and outlier exists, the first one is Jackknife Regression (JR) based on left out method, the second is Ridge Regression (RR) based on the addition of shrinking parameter, and the third is Latent Root Regression (LRR) by adding the latent root and latent vector. In the application, model parameters, standard errors, length of confidence intervals (L.C.I), coefficients of determination ( 2 R ), and mean square error (MSE) of these methods are estimated. Next, the perfomance of these three methods are compared with OLS by using the MSE and 2 R .Based on the analysis, LRR method was the best method compared to other methods since the value of MSE is less and 2 R is higher among others. The LRR was not only the best method when multicollinearity exist, but also was the best when the presence of both multicollinearity and outlier |
| first_indexed | 2024-03-06T02:20:34Z |
| format | Thesis |
| id | oai:ir.uitm.edu.my:32559 |
| institution | Universiti Teknologi MARA |
| language | English |
| last_indexed | 2024-03-06T02:20:34Z |
| publishDate | 2019 |
| record_format | dspace |
| spelling | oai:ir.uitm.edu.my:325592020-07-16T07:19:09Z https://ir.uitm.edu.my/id/eprint/32559/ Comparison of parameter estimation methods when multicollinearity and outlier exists / Aida Nurasikin Jamil ...[et al.] Jamil, Aida Nurasikin Abdul Muluk, Muhammad Fahmi Anuar, Nur Sabrina Abu Bakar, Mohamad Suffian H Social Sciences (General) Study and teaching. Research Ordinary Least Squares (OLS) estimator become worse in the presence of multicollinearity and outlier. Here, three methods are suggested to improve the model when multicollinearity and outlier exists, the first one is Jackknife Regression (JR) based on left out method, the second is Ridge Regression (RR) based on the addition of shrinking parameter, and the third is Latent Root Regression (LRR) by adding the latent root and latent vector. In the application, model parameters, standard errors, length of confidence intervals (L.C.I), coefficients of determination ( 2 R ), and mean square error (MSE) of these methods are estimated. Next, the perfomance of these three methods are compared with OLS by using the MSE and 2 R .Based on the analysis, LRR method was the best method compared to other methods since the value of MSE is less and 2 R is higher among others. The LRR was not only the best method when multicollinearity exist, but also was the best when the presence of both multicollinearity and outlier 2019-07 Thesis NonPeerReviewed text en https://ir.uitm.edu.my/id/eprint/32559/1/32559.pdf Comparison of parameter estimation methods when multicollinearity and outlier exists / Aida Nurasikin Jamil ...[et al.]. (2019) Degree thesis, thesis, Universiti Teknologi MARA, Kelantan. |
| spellingShingle | H Social Sciences (General) Study and teaching. Research Jamil, Aida Nurasikin Abdul Muluk, Muhammad Fahmi Anuar, Nur Sabrina Abu Bakar, Mohamad Suffian Comparison of parameter estimation methods when multicollinearity and outlier exists / Aida Nurasikin Jamil ...[et al.] |
| title | Comparison of parameter estimation methods when multicollinearity and outlier exists / Aida Nurasikin Jamil ...[et al.] |
| title_full | Comparison of parameter estimation methods when multicollinearity and outlier exists / Aida Nurasikin Jamil ...[et al.] |
| title_fullStr | Comparison of parameter estimation methods when multicollinearity and outlier exists / Aida Nurasikin Jamil ...[et al.] |
| title_full_unstemmed | Comparison of parameter estimation methods when multicollinearity and outlier exists / Aida Nurasikin Jamil ...[et al.] |
| title_short | Comparison of parameter estimation methods when multicollinearity and outlier exists / Aida Nurasikin Jamil ...[et al.] |
| title_sort | comparison of parameter estimation methods when multicollinearity and outlier exists aida nurasikin jamil et al |
| topic | H Social Sciences (General) Study and teaching. Research |
| url | https://ir.uitm.edu.my/id/eprint/32559/1/32559.pdf |
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