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Inventi Impact: Modeling & Simulation

Articles

  • Inventi:ems/32119/20
    Variable Selection via Biased Estimators in the Linear Regression Model
    Research 2020 : July - September
    Manickavasagar Kayanan, Pushpakanthie Wijekoon

    Least Absolute Shrinkage and Selection Operator (LASSO) is used for variable\nselection as well as for handling the multicollinearity problem simultaneously\nin the linear regression model. LASSO produces estimates having\nhigh variance if the number of predictors is higher than the number of observations\nand if high multicollinearity exists among the predictor variables.\nTo handle this problem, Elastic Net (ENet) estimator was introduced by\ncombining LASSO and Ridge estimator (RE)..........................

    How to Cite this Article
    Attribution/ CC Compliant Citation: Kayanan, M. and Wijekoon, P. (2020) Variable Selection via Biased Estimators in the Linear\nRegression Model. Open Journal of Statistics, 10, 113-126.\nhttps://doi.org/10.4236/ojs.2020.101009\nhttp://creativecommons.org/licenses/by/4.0/\n\nSome formatting elements, header, footer, logos, dates and pagination were modified while adapting this article.
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