Improved LARS algorithm for adaptive LASSO in the linear regression model
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909233698570240 |
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| author | Kayanan, Manickavasagar Wijekoon, Pushpakanthie |
| author_facet | Kayanan, Manickavasagar Wijekoon, Pushpakanthie |
| contents | The adaptive LASSO has been used for consistent variable selection in place of LASSO in the linear regression model. In this article, we propose a modified LARS algorithm to combine adaptive LASSO with some biased estimators, namely the Almost Unbiased Ridge Estimator (AURE), Liu Estimator (LE), Almost Unbiased Liu Estimator (AULE), Principal Component Regression Estimator (PCRE), r-k class estimator, and r-d class estimator. Furthermore, we examine the performance of the proposed algorithm using a Monte Carlo simulation study and real-world examples. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_07985 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Improved LARS algorithm for adaptive LASSO in the linear regression model Kayanan, Manickavasagar Wijekoon, Pushpakanthie Methodology The adaptive LASSO has been used for consistent variable selection in place of LASSO in the linear regression model. In this article, we propose a modified LARS algorithm to combine adaptive LASSO with some biased estimators, namely the Almost Unbiased Ridge Estimator (AURE), Liu Estimator (LE), Almost Unbiased Liu Estimator (AULE), Principal Component Regression Estimator (PCRE), r-k class estimator, and r-d class estimator. Furthermore, we examine the performance of the proposed algorithm using a Monte Carlo simulation study and real-world examples. |
| title | Improved LARS algorithm for adaptive LASSO in the linear regression model |
| topic | Methodology |
| url | https://arxiv.org/abs/2405.07985 |