Asar, YasinGenc, Asir2020-03-262020-03-2620160361-09181532-4141https://dx.doi.org/10.1080/03610918.2014.995815https://hdl.handle.net/20.500.12395/33827The binary logistic regression is a widely used statistical method when the dependent variable has two categories. In most of the situations of logistic regression, independent variables are collinear which is called the multicollinearity problem. It is known that multicollinearity affects the variance of maximum likelihood estimator (MLE) negatively. Therefore, this article introduces new shrinkage parameters for the Liu-type estimators in the Liu (2003) in the logistic regression model defined by Huang (2012) in order to decrease the variance and overcome the problem of multicollinearity. A Monte Carlo study is designed to show the goodness of the proposed estimators over MLE in the sense of mean squared error (MSE) and mean absolute error (MAE). Moreover, a real data case is given to demonstrate the advantages of the new shrinkage parameters.en10.1080/03610918.2014.995815info:eu-repo/semantics/closedAccessLogistic regressionMLEMulticollinearityShrinkage parameterPrimary 62J07Secondary 62J02Article45310941103Q3WOS:000372527900019Q4