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Performance Comparison of Penalized Regression Method in Logistic Regression for High-dimensional parse Data with Multicollinearity |
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| รหัสดีโอไอ | |
| Creator | Benjamas Tulyanitikul |
| Title | Performance Comparison of Penalized Regression Method in Logistic Regression for High-dimensional parse Data with Multicollinearity |
| Contributor | Supranee Lisawadi, Warangkhana Watcharasatian |
| Publisher | Thammasat University |
| Publication Year | 2563 |
| Journal Title | Thai Journal of Science and Technology |
| Journal Vol. | 9 |
| Journal No. | 6 |
| Page no. | 761-772 |
| Keyword | penalized regression, high-dimensional data, high-correlation, ridge regression, LASSO, adaptive LASSO |
| URL Website | https://www.tci-thaijo.org/ |
| Website title | THAIJO |
| ISSN | 2286-7333 |
| Abstract | Nowadays, technology is widely developed. The growth in high technology affects data science processes. One effect is that more data can be collected in a shorter time than before. This can be used in analyses. Analysts need to find an appropriate method to analyze the extensive data. The analyst should use the proper methodology for data of considerable size and high dimensions. One approach is penalized regression. That is a method for estimated coefficient parameters, variable selection, and the multicollinearity problem when the predictor variables are correlated. This study considers estimations for a logistic regression model with high-dimensional sparse data (n < p) and high correlation. We apply estimators from the penalized regression method: ridge regression, LASSO, and adaptive LASSO. These can be used to estimate coefficient parameters in high-dimensional data and could solve the multicollinearity problem. We compared the performance of these estimators. The performance in terms of the mean of prediction mean square error (mPMSE) using Monte Carlo simulation. The result showed that the adaptive LASSO estimator has the lowest mPMSE. Overall, adaptive LASSO performed better than ridge regression or LASSO. |
