Stochastic Restricted LASSO-Type Estimator in the Linear Regression Model

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dc.contributor.author Kayanan, M.
dc.contributor.author Wijekoon, P.
dc.date.accessioned 2022-05-11T12:43:02Z
dc.date.available 2022-05-11T12:43:02Z
dc.date.issued 30-03-20
dc.identifier.uri http://drr.vau.ac.lk/handle/123456789/90
dc.description.abstract Among several variable selection methods, LASSO is the most desirable estimation procedure for handling regularization and variable selection simultaneously in the high-dimensional linear regression models when multicollinearity exists among the predictor variables. Since LASSO is unstable under high multicollinearity, the elastic-net (Enet) estimator has been used to overcome this issue. According to the literature, the estimation of regression parameters can be improved by adding prior information about regression coefficients to the model, which is available in the form of exact or stochastic linear restrictions. In this article, we proposed a stochastic restricted LASSO-type estimator (SRLASSO) by incorporating stochastic linear restrictions. Furthermore, we compared the performance of SRLASSO with LASSO and Enet in root mean square error (RMSE) criterion and mean absolute prediction error (MAPE) criterion based on a Monte Carlo simulation study. Finally, a real-world example was used to demonstrate the performance of SRLASSO. en_US
dc.language.iso en en_US
dc.publisher Hindawi en_US
dc.subject LASSO en_US
dc.subject SRLASSO en_US
dc.subject Root mean square error en_US
dc.subject Mean absolute prediction error en_US
dc.title Stochastic Restricted LASSO-Type Estimator in the Linear Regression Model en_US
dc.type Article en_US
dc.identifier.doi https://doi.org/10.1155/2020/7352097 en_US
dc.identifier.journal Journal of Probability and Statistics en_US


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