New Two-Parameter Ridge Estimators for Addressing Multicollinearity in Linear Regression: Theory, Simulation, and Applications
Year: 2026
Type: article
Abstract: Multicollinearity among explanatory variables often undermines the reliability of the ordinary least squares (OLS) estimator that can be used in linear regression modeling. To overcome the limitation, a variety of two-parameter estimation strategies have been developed in prior research. We revisit these existing methods and present a newly established two-parameter ridge estimator to improve the accuracy of regression coefficients in terms of multicollinearity settings. A theoretical evaluation, assessed under the mean squared error (MSE) framework, is examined to compare their efficiency. Furthermore, a comprehensive simulation study is conducted to examine the empirical properties of all these estimators for different configurations, followed by a real-life dataset to examine their performance.
Source: Stats
DOI: 10.3390/stats9010015
Institution: Florida International University
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,930
OpenAlex ID: W7128480718
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