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.

翻译：自适应LASSO已被用于线性回归模型中替代LASSO进行一致的变量选择。本文提出一种改进的LARS算法，将自适应LASSO与若干有偏估计量相结合，包括几乎无偏岭估计量（AURE）、Liu估计量（LE）、几乎无偏Liu估计量（AULE）、主成分回归估计量（PCRE）、r-k类估计量和r-d类估计量。此外，我们通过蒙特卡洛模拟研究和实际数据示例检验了所提算法的性能。