What question did this study set out to answer?

This research aims to introduce a new class of biased estimators called Ridge-type Estimators (RTE) for linear regression models to handle multicollinearity.

March 18, 2026Revstat-statistical JournalOpen Access

A New General Ridge-Type Estimator in Linear Regression Models

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This research aims to introduce a new class of biased estimators called Ridge-type Estimators (RTE) for linear regression models to handle multicollinearity.
Development of general Ridge-type Estimators (RTE) combining properties of Ridge and Liu Estimators.
Definition of a functional relationship between biasing parameters for optimality.
Conduct of two Monte Carlo simulation studies to compare RTE with existing biased estimators.
Presentation of a numerical example to showcase RTE's superiority.
Ridge-type Estimators (RTE) demonstrated better performance compared to other biased estimators.
RTE showed reduced matrix mean square error in the presence of multicollinearity.
Simulation studies confirmed the robustness of RTE across various scenarios.

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Implication

Researchers develop and evaluate a new estimator to improve regression analysis, suggesting better performance under multicollinearity.

Cite This Study

Akay et al. (2023) studied this question.

synapsesocial.com/papers/69ba43e94e9516ffd37a59d6 https://doi.org/10.57805/revstat.v24i1.605

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A New General Ridge-Type Estimator in Linear Regression Models

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Discussion

Implication

Cite This Study

A New General Ridge-Type Estimator in Linear Regression Models

Key Points

Discussion

Implication

Cite This Study

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