TY  - JOUR
T1  - A New Kibria-Lukman-Type Estimator for Poisson Regression Models
AU  - Çiçek, Cemal
AU  - Akay, Kadri Ulaş
PY  - 2024
DA  - December
Y2  - 2024
DO  - 10.26650/acin.1558583
JF  - Acta Infologica
JO  - ACIN
PB  - Istanbul University
WT  - DergiPark
SN  - 2602-3563
SP  - 199
EP  - 212
VL  - 8
IS  - 2
LA  - en
AB  - One of the most important models for the analysis of count data is the Poisson Regression Model (PRM). The parameter estimates of the PRM are obtained by the Maximum Likelihood Estimator (MLE). However, MLE is adversely affected in the presence of multicollinearity, which is known as the approximately linear relationship between the explanatory variables. Many shrinkage estimators have been proposed to reduce the effects of multicollinearity in PRMs. As an alternative to other biased estimators that are already in use in PRMs, we presented a novel estimator in this paper that is based on the Kibria-Lukman estimator. The superiority of the proposed new biased estimator over existing biased estimators is given by the asymptotic matrix mean square error. Furthermore, two separate Monte Carlo simulation studies are conducted to investigate the performance of the proposed biased estimators. Finally, real data is used to examine the superiority of the proposed estimator.
KW  - Mean squared error
KW  - multicollinearity
KW  - poisson liu estimator
KW  - poisson regression
KW  - poisson ridge estimator
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UR  - https://doi.org/10.26650/acin.1558583
L1  - https://dergipark.org.tr/en/download/article-file/4251707
ER  -