improved parameters estimation in the multicollinear poisson regression model based on stein-liu estimators

This paper addressed parameter estimation in the poisson regression model in the presence of multicollinearity when it is surmised that the parameter vector is restricted to a linear subspace‎. ‎to improve the efficiency of parameter estimation‎, ‎we proposed the stein-liu and positive stein-liu strategies‎. ‎the proposed estimators' asymptotic distributional biases and variances were derived‎, ‎and their variances were compared‎. ‎the performance of the proposed estimators was investigated through an extensive monte carlo simulation study‎. ‎the suggested estimators were also applied to data from swedish football‎. ‎the results confirmed that the performances of our estimators were superior to the unrestricted liu estimator‎. ‎as an important result‎, ‎the stein-liu estimators uniformly perform better than the unrestricted liu estimator