James-Stein type estimators in beta regression model‎: simulation and application‎

Recently‎, ‎the beta regression model has been used in several fields of science to data the form rate or proportion‎. ‎In this paper‎, ‎we propose some novel and improved methods estimate parameters model‎. ‎We consider a sub-space on coefficients combine unrestricted restricted estimators then we present Stein-type preliminary estimators‎. develop expressions for proposed estimators' asymptotic biases their quadratic risks‎. ‎Numerical studies through Monte Carlo simulations are evaluate performance terms simulated relative efficiency‎. ‎The results show that outperform estimator when restrictions hold‎. ‎Finally‎, ‎an empirical application is provided demonstrate practical usefulness ‎estimators.‎