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Regularized Nonlinear Least Trimmed Squares Estimator in the Presence of Multicollinearity and Outliers
2018
American Journal of Theoretical and Applied Statistics
This study proposes a regularized robust Nonlinear Least Trimmed squares estimator that relies on an Elastic net penalty in nonlinear regression. Regularization parameter selection was done using a robust cross-validation criterion and estimation through Newton Raphson iteration algorthm for the oprimal model coefficients. Monte Carlo simulation was conducted to verify the theoretical properties outlined in the methodology both for scenarios of presence and absence of multicollinearity and
doi:10.11648/j.ajtas.20180704.14
fatcat:gh2imz7ftrhy7iykgytrogjjd4