Asar, YasinKorkmaz, Merve2024-02-232024-02-2320220377-04271879-1778https://doi.org/10.1016/j.cam.2021.113819https://hdl.handle.net/20.500.12452/11679The Liu-type estimator has been consistently demonstrated to be an attractive shrinkage method to reduce the effect of multicollinearity problem. It is known that multicollinear-ity affects the variance of the maximum likelihood estimator negatively in gamma regression model. Therefore, an almost unbiased Liu-type estimator together with a modified version of it is proposed to overcome the multicollinearity problem. The performance of the new estimators is investigated both theoretically and numerically via a Monte Carlo simulation experiment and a real data illustration. Based on the results, it is observed that the proposed estimators can bring significant improvement relative to other competitor estimators. (c) 2021 Elsevier B.V. All rights reserved.eninfo:eu-repo/semantics/closedAccessAlmost Unbiased Liu Type EstimatorGamma RegressionLiu Type EstimatorMulticollinearityModified Almost Unbiased Liu Type EstimatorMonte Carlo SimulationArticle4032-s2.0-85116322385Q2WOS:000710203600001Q110.1016/j.cam.2021.113819