a simulation comparison of Ridge regression estimators with Lars
Introduction Regression analysis is a common method for modeling relationships between variables. Usually Ordinary Least Squares method is applied to estimate regression model parameters. These estimators are unbiased and appropriate when design matrix is nonsingular. In presence of multicollinearit...
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| Autor*in: |
Roshanak Alimohammadi [verfasserIn] Jaleh Bahari [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Persisch |
| Erschienen: |
2022 |
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In: پژوهشهای ریاضی - Kharazmi University, 2023, 8(2022), 2, Seite 152-164 |
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| Übergeordnetes Werk: |
volume:8 ; year:2022 ; number:2 ; pages:152-164 |
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DOAJ088637425 |
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| 520 | |a Introduction Regression analysis is a common method for modeling relationships between variables. Usually Ordinary Least Squares method is applied to estimate regression model parameters. These estimators are unbiased and appropriate when design matrix is nonsingular. In presence of multicollinearity, design matrix is singular and Ordinary Least Squares estimates cannot be obtained. In this situation, other methods, such as Lasso, Ridge and Lars may be considered. Other hand, in many fields such as medicine, number of variables is greater than the number of observations and usual methods such as Ordinary Least Squares are not proper and shrinkage methods, such as Lasso, Ridge and ... have a better performance to estimate regression model coefficients. In the shrinkage methods, tuning parameter plays an essential role in selecting variables and estimating parameters. Bridge shrinkage estimators is an estimator that can be obtained by changing its tuning parameter. In other words, Bridge method is the extension of Ridge and Lasso regression methods. Selecting the appropriate amount of tuning parameter is important. There are many studies on each of these methods under the assumed conditions. In this paper, performance of Bridge shrinkage estimators, such as Lasso and Ridge are compared with Lars and Ordinary Least Squares estimators in a simulation study. Material and Methods A simulation study is applied to compare performance of the regression methods Ridge, Lasso, Lars and Ordinary Least Squares. MSE criterion is applied to evaluate the method performance. Statistical software R is applied for simulation and comparing the regression methods. Results and discussion In the presence of collinearity, Bridge regression estimators will result in appropriate estimators. These estimators are biased but their performance is better than unbiased estimators such as Ordinary Least Squares. Indeed, Bridge estimators have the best performance in the class of biased estimators. Conclusion In this article, Ridge and Lasso estimators as special cases of Bridge estimators are compared with Lasso and Ordinary Least Squares in a simulation study. This study shows that under the supposed conditions, Ridge, Lasso and Lars have better action than Ordinary Least Squares method. Lars method has the best performance and Ridge estimators is better than Lasso Regression. | ||
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(DE-627)DOAJ088637425 (DE-599)DOAJ2137e1f623cc4789bd74a210b59cdc59 DE-627 ger DE-627 rakwb per QA1-939 Roshanak Alimohammadi verfasserin aut a simulation comparison of Ridge regression estimators with Lars 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Introduction Regression analysis is a common method for modeling relationships between variables. Usually Ordinary Least Squares method is applied to estimate regression model parameters. These estimators are unbiased and appropriate when design matrix is nonsingular. In presence of multicollinearity, design matrix is singular and Ordinary Least Squares estimates cannot be obtained. In this situation, other methods, such as Lasso, Ridge and Lars may be considered. Other hand, in many fields such as medicine, number of variables is greater than the number of observations and usual methods such as Ordinary Least Squares are not proper and shrinkage methods, such as Lasso, Ridge and ... have a better performance to estimate regression model coefficients. In the shrinkage methods, tuning parameter plays an essential role in selecting variables and estimating parameters. Bridge shrinkage estimators is an estimator that can be obtained by changing its tuning parameter. In other words, Bridge method is the extension of Ridge and Lasso regression methods. Selecting the appropriate amount of tuning parameter is important. There are many studies on each of these methods under the assumed conditions. In this paper, performance of Bridge shrinkage estimators, such as Lasso and Ridge are compared with Lars and Ordinary Least Squares estimators in a simulation study. Material and Methods A simulation study is applied to compare performance of the regression methods Ridge, Lasso, Lars and Ordinary Least Squares. MSE criterion is applied to evaluate the method performance. Statistical software R is applied for simulation and comparing the regression methods. Results and discussion In the presence of collinearity, Bridge regression estimators will result in appropriate estimators. These estimators are biased but their performance is better than unbiased estimators such as Ordinary Least Squares. Indeed, Bridge estimators have the best performance in the class of biased estimators. Conclusion In this article, Ridge and Lasso estimators as special cases of Bridge estimators are compared with Lasso and Ordinary Least Squares in a simulation study. This study shows that under the supposed conditions, Ridge, Lasso and Lars have better action than Ordinary Least Squares method. Lars method has the best performance and Ridge estimators is better than Lasso Regression. ridge regression bridge regression lasso regression lars regression tuning parameter ordinary least squares error regression. Mathematics Jaleh Bahari verfasserin aut In پژوهشهای ریاضی Kharazmi University, 2023 8(2022), 2, Seite 152-164 (DE-627)DOAJ08719435X 25882554 nnns volume:8 year:2022 number:2 pages:152-164 https://doaj.org/article/2137e1f623cc4789bd74a210b59cdc59 kostenfrei http://mmr.khu.ac.ir/article-1-2942-en.html kostenfrei https://doaj.org/toc/2588-2546 Journal toc kostenfrei https://doaj.org/toc/2588-2554 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_342 AR 8 2022 2 152-164 |
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(DE-627)DOAJ088637425 (DE-599)DOAJ2137e1f623cc4789bd74a210b59cdc59 DE-627 ger DE-627 rakwb per QA1-939 Roshanak Alimohammadi verfasserin aut a simulation comparison of Ridge regression estimators with Lars 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Introduction Regression analysis is a common method for modeling relationships between variables. Usually Ordinary Least Squares method is applied to estimate regression model parameters. These estimators are unbiased and appropriate when design matrix is nonsingular. In presence of multicollinearity, design matrix is singular and Ordinary Least Squares estimates cannot be obtained. In this situation, other methods, such as Lasso, Ridge and Lars may be considered. Other hand, in many fields such as medicine, number of variables is greater than the number of observations and usual methods such as Ordinary Least Squares are not proper and shrinkage methods, such as Lasso, Ridge and ... have a better performance to estimate regression model coefficients. In the shrinkage methods, tuning parameter plays an essential role in selecting variables and estimating parameters. Bridge shrinkage estimators is an estimator that can be obtained by changing its tuning parameter. In other words, Bridge method is the extension of Ridge and Lasso regression methods. Selecting the appropriate amount of tuning parameter is important. There are many studies on each of these methods under the assumed conditions. In this paper, performance of Bridge shrinkage estimators, such as Lasso and Ridge are compared with Lars and Ordinary Least Squares estimators in a simulation study. Material and Methods A simulation study is applied to compare performance of the regression methods Ridge, Lasso, Lars and Ordinary Least Squares. MSE criterion is applied to evaluate the method performance. Statistical software R is applied for simulation and comparing the regression methods. Results and discussion In the presence of collinearity, Bridge regression estimators will result in appropriate estimators. These estimators are biased but their performance is better than unbiased estimators such as Ordinary Least Squares. Indeed, Bridge estimators have the best performance in the class of biased estimators. Conclusion In this article, Ridge and Lasso estimators as special cases of Bridge estimators are compared with Lasso and Ordinary Least Squares in a simulation study. This study shows that under the supposed conditions, Ridge, Lasso and Lars have better action than Ordinary Least Squares method. Lars method has the best performance and Ridge estimators is better than Lasso Regression. ridge regression bridge regression lasso regression lars regression tuning parameter ordinary least squares error regression. Mathematics Jaleh Bahari verfasserin aut In پژوهشهای ریاضی Kharazmi University, 2023 8(2022), 2, Seite 152-164 (DE-627)DOAJ08719435X 25882554 nnns volume:8 year:2022 number:2 pages:152-164 https://doaj.org/article/2137e1f623cc4789bd74a210b59cdc59 kostenfrei http://mmr.khu.ac.ir/article-1-2942-en.html kostenfrei https://doaj.org/toc/2588-2546 Journal toc kostenfrei https://doaj.org/toc/2588-2554 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_342 AR 8 2022 2 152-164 |
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(DE-627)DOAJ088637425 (DE-599)DOAJ2137e1f623cc4789bd74a210b59cdc59 DE-627 ger DE-627 rakwb per QA1-939 Roshanak Alimohammadi verfasserin aut a simulation comparison of Ridge regression estimators with Lars 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Introduction Regression analysis is a common method for modeling relationships between variables. Usually Ordinary Least Squares method is applied to estimate regression model parameters. These estimators are unbiased and appropriate when design matrix is nonsingular. In presence of multicollinearity, design matrix is singular and Ordinary Least Squares estimates cannot be obtained. In this situation, other methods, such as Lasso, Ridge and Lars may be considered. Other hand, in many fields such as medicine, number of variables is greater than the number of observations and usual methods such as Ordinary Least Squares are not proper and shrinkage methods, such as Lasso, Ridge and ... have a better performance to estimate regression model coefficients. In the shrinkage methods, tuning parameter plays an essential role in selecting variables and estimating parameters. Bridge shrinkage estimators is an estimator that can be obtained by changing its tuning parameter. In other words, Bridge method is the extension of Ridge and Lasso regression methods. Selecting the appropriate amount of tuning parameter is important. There are many studies on each of these methods under the assumed conditions. In this paper, performance of Bridge shrinkage estimators, such as Lasso and Ridge are compared with Lars and Ordinary Least Squares estimators in a simulation study. Material and Methods A simulation study is applied to compare performance of the regression methods Ridge, Lasso, Lars and Ordinary Least Squares. MSE criterion is applied to evaluate the method performance. Statistical software R is applied for simulation and comparing the regression methods. Results and discussion In the presence of collinearity, Bridge regression estimators will result in appropriate estimators. These estimators are biased but their performance is better than unbiased estimators such as Ordinary Least Squares. Indeed, Bridge estimators have the best performance in the class of biased estimators. Conclusion In this article, Ridge and Lasso estimators as special cases of Bridge estimators are compared with Lasso and Ordinary Least Squares in a simulation study. This study shows that under the supposed conditions, Ridge, Lasso and Lars have better action than Ordinary Least Squares method. Lars method has the best performance and Ridge estimators is better than Lasso Regression. ridge regression bridge regression lasso regression lars regression tuning parameter ordinary least squares error regression. Mathematics Jaleh Bahari verfasserin aut In پژوهشهای ریاضی Kharazmi University, 2023 8(2022), 2, Seite 152-164 (DE-627)DOAJ08719435X 25882554 nnns volume:8 year:2022 number:2 pages:152-164 https://doaj.org/article/2137e1f623cc4789bd74a210b59cdc59 kostenfrei http://mmr.khu.ac.ir/article-1-2942-en.html kostenfrei https://doaj.org/toc/2588-2546 Journal toc kostenfrei https://doaj.org/toc/2588-2554 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_342 AR 8 2022 2 152-164 |
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Roshanak Alimohammadi |
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a simulation comparison of Ridge regression estimators with Lars |
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a simulation comparison of Ridge regression estimators with Lars |
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Roshanak Alimohammadi Jaleh Bahari |
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simulation comparison of ridge regression estimators with lars |
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a simulation comparison of Ridge regression estimators with Lars |
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Introduction Regression analysis is a common method for modeling relationships between variables. Usually Ordinary Least Squares method is applied to estimate regression model parameters. These estimators are unbiased and appropriate when design matrix is nonsingular. In presence of multicollinearity, design matrix is singular and Ordinary Least Squares estimates cannot be obtained. In this situation, other methods, such as Lasso, Ridge and Lars may be considered. Other hand, in many fields such as medicine, number of variables is greater than the number of observations and usual methods such as Ordinary Least Squares are not proper and shrinkage methods, such as Lasso, Ridge and ... have a better performance to estimate regression model coefficients. In the shrinkage methods, tuning parameter plays an essential role in selecting variables and estimating parameters. Bridge shrinkage estimators is an estimator that can be obtained by changing its tuning parameter. In other words, Bridge method is the extension of Ridge and Lasso regression methods. Selecting the appropriate amount of tuning parameter is important. There are many studies on each of these methods under the assumed conditions. In this paper, performance of Bridge shrinkage estimators, such as Lasso and Ridge are compared with Lars and Ordinary Least Squares estimators in a simulation study. Material and Methods A simulation study is applied to compare performance of the regression methods Ridge, Lasso, Lars and Ordinary Least Squares. MSE criterion is applied to evaluate the method performance. Statistical software R is applied for simulation and comparing the regression methods. Results and discussion In the presence of collinearity, Bridge regression estimators will result in appropriate estimators. These estimators are biased but their performance is better than unbiased estimators such as Ordinary Least Squares. Indeed, Bridge estimators have the best performance in the class of biased estimators. Conclusion In this article, Ridge and Lasso estimators as special cases of Bridge estimators are compared with Lasso and Ordinary Least Squares in a simulation study. This study shows that under the supposed conditions, Ridge, Lasso and Lars have better action than Ordinary Least Squares method. Lars method has the best performance and Ridge estimators is better than Lasso Regression. |
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Introduction Regression analysis is a common method for modeling relationships between variables. Usually Ordinary Least Squares method is applied to estimate regression model parameters. These estimators are unbiased and appropriate when design matrix is nonsingular. In presence of multicollinearity, design matrix is singular and Ordinary Least Squares estimates cannot be obtained. In this situation, other methods, such as Lasso, Ridge and Lars may be considered. Other hand, in many fields such as medicine, number of variables is greater than the number of observations and usual methods such as Ordinary Least Squares are not proper and shrinkage methods, such as Lasso, Ridge and ... have a better performance to estimate regression model coefficients. In the shrinkage methods, tuning parameter plays an essential role in selecting variables and estimating parameters. Bridge shrinkage estimators is an estimator that can be obtained by changing its tuning parameter. In other words, Bridge method is the extension of Ridge and Lasso regression methods. Selecting the appropriate amount of tuning parameter is important. There are many studies on each of these methods under the assumed conditions. In this paper, performance of Bridge shrinkage estimators, such as Lasso and Ridge are compared with Lars and Ordinary Least Squares estimators in a simulation study. Material and Methods A simulation study is applied to compare performance of the regression methods Ridge, Lasso, Lars and Ordinary Least Squares. MSE criterion is applied to evaluate the method performance. Statistical software R is applied for simulation and comparing the regression methods. Results and discussion In the presence of collinearity, Bridge regression estimators will result in appropriate estimators. These estimators are biased but their performance is better than unbiased estimators such as Ordinary Least Squares. Indeed, Bridge estimators have the best performance in the class of biased estimators. Conclusion In this article, Ridge and Lasso estimators as special cases of Bridge estimators are compared with Lasso and Ordinary Least Squares in a simulation study. This study shows that under the supposed conditions, Ridge, Lasso and Lars have better action than Ordinary Least Squares method. Lars method has the best performance and Ridge estimators is better than Lasso Regression. |
| abstract_unstemmed |
Introduction Regression analysis is a common method for modeling relationships between variables. Usually Ordinary Least Squares method is applied to estimate regression model parameters. These estimators are unbiased and appropriate when design matrix is nonsingular. In presence of multicollinearity, design matrix is singular and Ordinary Least Squares estimates cannot be obtained. In this situation, other methods, such as Lasso, Ridge and Lars may be considered. Other hand, in many fields such as medicine, number of variables is greater than the number of observations and usual methods such as Ordinary Least Squares are not proper and shrinkage methods, such as Lasso, Ridge and ... have a better performance to estimate regression model coefficients. In the shrinkage methods, tuning parameter plays an essential role in selecting variables and estimating parameters. Bridge shrinkage estimators is an estimator that can be obtained by changing its tuning parameter. In other words, Bridge method is the extension of Ridge and Lasso regression methods. Selecting the appropriate amount of tuning parameter is important. There are many studies on each of these methods under the assumed conditions. In this paper, performance of Bridge shrinkage estimators, such as Lasso and Ridge are compared with Lars and Ordinary Least Squares estimators in a simulation study. Material and Methods A simulation study is applied to compare performance of the regression methods Ridge, Lasso, Lars and Ordinary Least Squares. MSE criterion is applied to evaluate the method performance. Statistical software R is applied for simulation and comparing the regression methods. Results and discussion In the presence of collinearity, Bridge regression estimators will result in appropriate estimators. These estimators are biased but their performance is better than unbiased estimators such as Ordinary Least Squares. Indeed, Bridge estimators have the best performance in the class of biased estimators. Conclusion In this article, Ridge and Lasso estimators as special cases of Bridge estimators are compared with Lasso and Ordinary Least Squares in a simulation study. This study shows that under the supposed conditions, Ridge, Lasso and Lars have better action than Ordinary Least Squares method. Lars method has the best performance and Ridge estimators is better than Lasso Regression. |
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