New estimators for the probit regression model with multicollinearity

The probit regression model (PRORM) aims to model a binary response with one or more explanatory variables. The parameter of the PRORM is estimated using an estimation method called the maximum likelihood (ML), like a logistic model. When multicollinear-ity exists, ML performance suffers. In this case, we propose two estimators (the probit modified ridge and probit Dawoud -Kibria estimators) for the PRORM. To assess the pro-posed estimators' superiority, we have some theoretical comparisons of the proposed pro -bit Dawoud -Kibria estimator with the ML, probit ridge, probit Liu, and probit modified ridge estimators via the mean squared error. A simulation study is offered with several criteria for examining the effectiveness of the suggested estimators' efficiency. In addition, a real-life application is applied to confirm the proposed estimators' efficiency. The results of the simulation and application indicated that the proposed estimators outperformed ML and probit ridge estimators, especially when there was a strong correlation between the explanatory variables.(c) 2023 The Author(s). Published by Elsevier B.V. on behalf of African Institute of Mathematical Sciences / Next Einstein Initiative. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )