Ertan, EsraGiresunlu, İsmail MüfütAkay, Kadri Ulaş2024-07-122024-07-122019Ertan, E, Giresunlu, İ. M. ve Akay, K. U. (2019). Improved new liu-type estimator for poisson regression models. International Conference of Mathematical Sciences (ICMS 2019). s. 187.978-605-2124-29-1https://hdl.handle.net/20.500.12415/2145Poisson regression models are commonly used in applied sciences such as economics and the social sciences when analyzing the count data. The maximum likelihood method is the well-known estimation technique to estimate the parameters in Poisson regression models. However, when the independent variables are highly intercorrelated, unstable parameter estimates are obtained. Therefore, biased estimators are widely used to alleviate the undesirable effects of these problems. In this study, we proposed a new improved Liu-type estimator as an alternative to other proposed biased estimators. The superiority of the new biased estimator over the existing biased estimators are given under the asymptotic matrix mean square error criterion. Furthermore, Monte Carlo simulation studies are executed to compare the performances of the proposed biased estimators. Finally, the obtained results are illustrated in real data.enCC0 1.0 Universalinfo:eu-repo/semantics/openAccessPoisson regression modelsBiased estimatorsMulticollinearityArticle188187