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The research aims to improve existing regression techniques to handle multicollinearity and outliers more effectively.

June 19, 2026StatsOpen Access

A Comparative Study of Robust and Improved Shrinkage Estimators Under Multicollinearity and Outliers Using Multiple Performance Criteria with Application to Health Data

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Comparative study evaluates enhanced estimators' predictive performance in health data, indicating improved reliability.

The research aims to improve existing regression techniques to handle multicollinearity and outliers more effectively.
Developed improved ridge and Liu-type estimators with robust variants.
Conducted Monte Carlo simulations to test various levels of multicollinearity and data contamination.
Applied multiple performance criteria for evaluation including mean squared error and Akaike information criterion.
The improved estimators showed reduced bias and variance compared to traditional methods.
Predictive performance was enhanced, particularly under severe multicollinearity and outliers.
Robust variants provided more stable estimates across health-related datasets.

Yasmin et al. (2026) studied this question.

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