Combining principal component and robust ridge estimators in linear regression model with multicollinearity and outlier

The method of least squared suffers a setback when there is multicollinearity and outliers in the linear regression model. In this article, we developed a new estimator to jointly handle multicollinearity and outliers by pooling the following estimators together: the M‐estimator, the principal compo...

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Veröffentlicht in:	Concurrency and computation 2022-05, Vol.34 (10), p.n/a
Hauptverfasser:	Arum, Kingsley Chinedu, Ugwuowo, Fidelis Ifeanyi
Format:	Artikel
Sprache:	eng
Schlagworte:	Estimators multicollinearity M‐estimator outliers principal component Regression analysis Regression models ridge estimator Robustness (mathematics)
Online-Zugang:	Volltext
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