What question did this study set out to answer?

The aim is to develop robust estimators that remain reliable in the presence of multicollinearity and outliers.

Weighted robust improved KL M-estimators for linear regression model in presence of multicollinearity and outliers: simulation and applications

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Simulation demonstrates improved M-estimators reduce error in regression models with multicollinearity and outliers, suggesting enhanced reliability.

The aim is to develop robust estimators that remain reliable in the presence of multicollinearity and outliers.
Introduced weighted robust improved Kibria–Lukman ridge M-estimators.
Utilized composite shrinkage functions with robust weighting methods.
Performed Monte Carlo simulations to evaluate estimator performance across different conditions.
The proposed SQD2 and SQD4 estimators show lower mean squared error compared to existing estimators.
SQD4 achieved up to 80% lower mean squared error than Kibria Lukman M-estimators under high multicollinearity with significant contamination.

Wasim et al. (2026) studied this question.

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Simulation demonstrates improved M-estimators reduce error in regression models with multicollinearity and outliers, suggesting enhanced reliability.