A modified ridge m-estimator for linear regression model with multicollinearity and outliers

Robust biased estimators for Poisson regression model: Simulation and applications

“…Literature has shown from the linear regression perspective that a model can suffer jointly from multicollinearity and outlier. Much attention has accounted for both problems in the linear regression model 23–28 . However, most studies in the Poisson regression model has not taken care of both problems.…”

“…Much attention has accounted for both problems in the linear regression model. [23][24][25][26][27][28] However, most studies in the Poisson regression model has not taken care of both problems. Recently, Abonazel and Dawoud 29 proposed a robust estimators to account for both problems by combining the weighted maximum likelihood estimator with the ridge estimator and the almost unbiased ridge estimator.…”

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

“…Attention has been given to both problems in a few studies. 18,[22][23][24][25][26] Silvapulle 25 combined the M-estimator and the ridge estimator to mitigate multicollinearity and outlier.…”

“…Literature has shown that a linear regression model can suffer jointly from multicollinearity and outlier. Attention has been given to both problems in a few studies 18,22–26 . Silvapulle 25 combined the M‐estimator and the ridge estimator to mitigate multicollinearity and outlier.…”

“…17,18 Robust regression estimator produced more reliable and stable estimates than LSE for linear regression models with outliers. [19][20][21][22][23][24] Examples include the M-estimator that is resistant to an outlier in the y-direction. 19…”