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This is a Quantitative Study study.

November 1, 2025Open Access

Finite Sample Performance of the Robust Stein Estimator in the Presence of Multicollinearity and Outliers

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Authors

LDLwando DlembulaUniversity of Fort Hare CMChioneso Show MarangeUniversity of Fort Hare LKLwando KondloUniversity of Fort Hare

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Implication

Analysis demonstrates that robust estimators effectively reduce root mean squared error in multicollinearity, suggesting improvements over classical methods.

Key Points

The robust Stein estimator reduces root mean squared error even with both multicollinearity and outliers present.
Monte Carlo simulations across varying conditions showed outperformance of the robust Stein estimator compared to classical methods.
Robust estimators leverage shrinkage techniques to handle outliers in both the x and y directions effectively.
Findings underscore the need for proper performance evaluation methods in the presence of multicollinearity and outliers.

Cite This Study

Dlembula et al. (2025) studied this question.

synapsesocial.com/papers/69054ffa1a99e50463de68de https://doi.org/10.20944/preprints202510.2381.v1

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Implication

Analysis demonstrates that robust estimators effectively reduce root mean squared error in multicollinearity, suggesting improvements over classical methods.

Finite Sample Performance of the Robust Stein Estimator in the Presence of Multicollinearity and Outliers

Discussion

Member takes

Implication

Key Points

Cite This Study

Finite Sample Performance of the Robust Stein Estimator in the Presence of Multicollinearity and Outliers

Discussion

Member takes

Implication

Key Points

Cite This Study

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