Improved LARS Algorithm for Adaptive LASSO in the Linear Regression Model
Manickavasagar Kayanan *
Department of Physical Science, University of Vavuniya, Vavuniya, Sri Lanka.
Pushpakanthie Wijekoon
Department of Statistics and Computer Science, University of Peradeniya, Peradeniya, Sri Lanka.
*Author to whom correspondence should be addressed.
Abstract
The adaptive LASSO method has been employed for reliable variable selection as an alternative to LASSO in linear regression models. This paper introduces an adjusted LARS algorithm that integrates adaptive LASSO with several biased estimators, including 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. The effectiveness of the proposed algorithm is evaluated through Monte Carlo simulation and empirical examples.
Keywords: Adaptive LASSO, LARS, biased estimators, monte carlo simulation