TY - JOUR TI - Two parameter Ridge estimator in the inverse Gaussian regression model AB - It is well known that multicollinearity, which occurs among the explanatory variables, has adverse effects on the maximum likelihood estimator in the inverse Gaussian regression model. Biased estimators are proposed to cope with the multicollinearity problem in the inverse Gaussian regression model. The main interest of this article is to introduce a new biased estimator. Also, we compare newly proposed estimator with the other estimators given in the literature. We conduct a Monte Carlo simulation and provide a real data example to illustrate the performance of the proposed estimator over the maximum likelihood and Ridge estimators. As a result of the simulation study and real data example, the newly proposed estimator is superior to the other estimators used in this study. AU - BULUT, Y. Murat AU - IŞILAR, MELİKE DO - 10.15672/hujms.813540 PY - 2021 JO - Hacettepe Journal of Mathematics and Statistics VL - 50 IS - 3 SN - 1303-5010 SP - 895 EP - 910 DB - TRDizin UR - http://search/yayin/detay/494741 ER -