Regression analysis

Minimum-variance unbiased estimator

Multicollinearity

Mathematics

Statistics

Mean squared error

Estimator

Bias of an estimator

Geography

Computer science

Trimmed estimator

Cartography

Ridge

Ordinary least squares

Journal of Chemometrics

The Linear regression model is one of the most widely used models in different fields of study. The most popularly used estimation technique is the ordinary least squares estimator. The technique becomes unstable and gives misleading result in the presence of multicollinearity. The ridge regression estimator has been widely accepted as an alternative method to combat the problem of multicollinearity. In this study, a modified ridge‐type estimator is suggested by modifying the ridge regression estimator. A Monte Carlo simulation study and real‐life application were conducted to compare the performance of this estimator and some other existing estimators. The results of both simulation study and real‐life application show that the proposed estimator outperforms other competing estimator.

Modified ridge‐type estimator to combat multicollinearity: Application to chemical data