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Generalized Stochastic Restricted LARS Algorithm

Authors:

Manickavasagar Kayanan ,

University of Peradeniya, Peradeniya, LK
About Manickavasagar

Postgraduate Institute of Science,

 

Department of Physical Science, University of Vavuniya, Vavuniya

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Pushpakanthie Wijekoon

University of Peradeniya, Peradeniya, LK
About Pushpakanthie
Department of Statistics and Computer Science
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Abstract

The Least Absolute Shrinkage and Selection Operator (LASSO) is used to tackle both the multicollinearity issue and the variable selection concurrently in the linear regression model. The Least Angle Regression (LARS) algorithm has been used widely to produce LASSO solutions. However, this algorithm is unreliable when high multicollinearity exists among regressor variables. One solution to improve the estimation of regression parameters when multicollinearity exists is adding preliminary information about the regression coefficient to the model as either exact linear restrictions or stochastic linear restrictions. Based on this solution, this article proposed a generalized version of the stochastic restricted LARS algorithm, which combines LASSO with existing stochastic restricted estimators. Further, we examined the performance of the proposed algorithm by employing a Monte Carlo simulation study and a numerical example.

How to Cite: Kayanan, M. and Wijekoon, P., 2022. Generalized Stochastic Restricted LARS Algorithm. Ruhuna Journal of Science, 13(1), pp.14–28. DOI: http://doi.org/10.4038/rjs.v13i1.112
Published on 18 Aug 2022.
Peer Reviewed

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