Comparison between best subset and lasso regression on consumer price index Malaysia
This research is aimed to determine the factors contributing to the prediction of the total Consumer Price Index (CPI) in Malaysia through model selection using two methods which are the best subset and LASSO regression. The outliers are identified using the leverage values and studentized delet...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2021
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/6507/ http://eprints.uthm.edu.my/6507/1/P13550_499a158fa53fb23c456f577fae1fa84c.pdf |
| _version_ | 1848888828855582720 |
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| author | Gopal Pillay, Khuneswari A/P Ravieb, Tivya Mohd Padzil, Siti Aisyah |
| author_facet | Gopal Pillay, Khuneswari A/P Ravieb, Tivya Mohd Padzil, Siti Aisyah |
| author_sort | Gopal Pillay, Khuneswari A/P |
| building | UTHM Institutional Repository |
| collection | Online Access |
| description | This research is aimed to determine the factors contributing to the prediction of the total Consumer Price Index
(CPI) in Malaysia through model selection using two methods which are the best subset and LASSO regression. The outliers
are identified using the leverage values and studentized deleted residuals while the multicollinearity variables will undergo
progressive elimination identified through Variance Inflation Factor (VIF) values. Both methods were compared using the
Mean Square Error of Prediction (MSE(P)) to find the best approach to display the CPI data. The model with the smallest
MSE(P) will be chosen as the best model. The result showed that the MSE(P) of the best model using both the best subset
regression and LASSO regression is almost the same. Therefore, the model selection using LASSO regression will be
chosen as the best approach due to the simple process in identifying the best model. The best LASSO model consists of
nine major categories such as food and non-alcoholic beverages (X1), alcoholic beverages and tobacco (X2), clothing and
footwear (X3), transport (X7), communication (X8), recreation service and culture (X9), education (X10), restaurants and
hotels (X11), miscellaneous goods and services (X12). |
| first_indexed | 2025-11-15T20:16:29Z |
| format | Conference or Workshop Item |
| id | uthm-6507 |
| institution | Universiti Tun Hussein Onn Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T20:16:29Z |
| publishDate | 2021 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uthm-65072022-02-28T00:25:29Z http://eprints.uthm.edu.my/6507/ Comparison between best subset and lasso regression on consumer price index Malaysia Gopal Pillay, Khuneswari A/P Ravieb, Tivya Mohd Padzil, Siti Aisyah HB221-236 Price This research is aimed to determine the factors contributing to the prediction of the total Consumer Price Index (CPI) in Malaysia through model selection using two methods which are the best subset and LASSO regression. The outliers are identified using the leverage values and studentized deleted residuals while the multicollinearity variables will undergo progressive elimination identified through Variance Inflation Factor (VIF) values. Both methods were compared using the Mean Square Error of Prediction (MSE(P)) to find the best approach to display the CPI data. The model with the smallest MSE(P) will be chosen as the best model. The result showed that the MSE(P) of the best model using both the best subset regression and LASSO regression is almost the same. Therefore, the model selection using LASSO regression will be chosen as the best approach due to the simple process in identifying the best model. The best LASSO model consists of nine major categories such as food and non-alcoholic beverages (X1), alcoholic beverages and tobacco (X2), clothing and footwear (X3), transport (X7), communication (X8), recreation service and culture (X9), education (X10), restaurants and hotels (X11), miscellaneous goods and services (X12). 2021 Conference or Workshop Item PeerReviewed text en http://eprints.uthm.edu.my/6507/1/P13550_499a158fa53fb23c456f577fae1fa84c.pdf Gopal Pillay, Khuneswari A/P and Ravieb, Tivya and Mohd Padzil, Siti Aisyah (2021) Comparison between best subset and lasso regression on consumer price index Malaysia. In: AIP Conference Proceedings, 18 Nov 2021. https://doi.org/10.1063/5.0075657 |
| spellingShingle | HB221-236 Price Gopal Pillay, Khuneswari A/P Ravieb, Tivya Mohd Padzil, Siti Aisyah Comparison between best subset and lasso regression on consumer price index Malaysia |
| title | Comparison between best subset and lasso regression on consumer price index Malaysia |
| title_full | Comparison between best subset and lasso regression on consumer price index Malaysia |
| title_fullStr | Comparison between best subset and lasso regression on consumer price index Malaysia |
| title_full_unstemmed | Comparison between best subset and lasso regression on consumer price index Malaysia |
| title_short | Comparison between best subset and lasso regression on consumer price index Malaysia |
| title_sort | comparison between best subset and lasso regression on consumer price index malaysia |
| topic | HB221-236 Price |
| url | http://eprints.uthm.edu.my/6507/ http://eprints.uthm.edu.my/6507/ http://eprints.uthm.edu.my/6507/1/P13550_499a158fa53fb23c456f577fae1fa84c.pdf |