Multicollinearity negatively affects the efficiency of the maximum likelihood estimator (MLE) in both the linear and generalized linear models. The Kibria and Lukman estimator (KLE) was developed as an alternative to the MLE to handle multicollinearity for the linear regression model. In this study, we proposed the Logistic Kibria-Lukman estimator (LKLE) to handle multicollinearity for the logistic regression model. We theoretically established the superiority condition of this new estimator over the MLE, the logistic ridge estimator (LRE), the logistic Liu estimator (LLE), the logistic Liu-type estimator (LLTE) and the logistic two-parameter estimator (LTPE) using the mean squared error criteria. The theoretical conditions were validated using a real-life dataset, and the results showed that the conditions were satisfied. Finally, a simulation and the real-life results showed that the new estimator outperformed the other considered estimators. However, the performance of the estimators was contingent on the adopted shrinkage parameter estimators.

This paper introduces a two-parameters generator of continuous statistical probability distributions called the Alpha Power Rayleigh-G (APRAY-G) family, some statistical properties of the family of distributions were derived, and we introduced a two-submodels of the generator. We estimate the parameters of the models based on the method of maximum likelihood estimation and explored simulation studies based on the introduced submodels. We observed that the biasedness and root mean square errors decrease as the sample size becomes large. We examined the applications of the models based on real-life data sets. We compared the obtained results with some existing probability distribution models. The results showed that the proposed models gave a better fitness to the data under investigation.