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Almost unbiased Liu-type estimators in gamma regression model
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NSTL
Elsevier
The 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.
Almost unbiased Liu type estimatorGamma regressionLiu type estimatorMulticollinearityModified almost unbiased Liu type estimatorMonte Carlo simulationRIDGE-REGRESSIONPERFORMANCESHRINKAGE
NSTL
Elsevier
