Regularized Nonlinear Least Trimmed Squares Estimator in the Presence of Multicollinearity and Outliers
American Journal of Theoretical and Applied Statistics
Volume 7, Issue 4, July 2018, Pages: 156-162
Received: Apr. 17, 2018;
Accepted: May 29, 2018;
Published: Jun. 29, 2018
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George Kemboi Kirui Keitany, Department of Statistics and Actuarial Science, Kenyatta University (KU), Nairobi, Kenya
Ananda Omutokoh Kube, Department of Statistics and Actuarial Science, Kenyatta University (KU), Nairobi, Kenya
Joseph Mutua Mutisya, Department of Statistics and Actuarial Science, Kenyatta University (KU), Nairobi, Kenya
Fundi Daniel Muriithi, Department of Statistics and Actuarial Science, Kenyatta University (KU), Nairobi, Kenya
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 existence of outliers. The proposed procedure performed well compared to the NLS and NLTS in a viewpoint of yielding relatively lower values of MSE and Bias. Furthermore, a real data analysis demonstrated satisfactory performance of the suggested technique.
George Kemboi Kirui Keitany,
Ananda Omutokoh Kube,
Joseph Mutua Mutisya,
Fundi Daniel Muriithi,
Regularized Nonlinear Least Trimmed Squares Estimator in the Presence of Multicollinearity and Outliers, American Journal of Theoretical and Applied Statistics.
Vol. 7, No. 4,
2018, pp. 156-162.
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