Abstract:
In this study, we introduce a new estimator named the Stochastic Restricted Modified Mixed Logistic Estimator (SRMMLE), which is specifically designed to handle multicollinearity within the framework of stochastic linear restrictions. Further, we enhance the SRMMLE by modifying its coefficients, resulting in four distinct variants: Stochastic Restricted Modified Mixed Logistic Estimator 1 (SRMMLE1), Stochastic Restricted Modified Mixed Logistic Estimator 2 (SRMMLE2), Stochastic Restricted Modified Mixed Logistic Estimator 3 (SRMMLE3), and Stochastic Restricted Modified Mixed Logistic Estimator 4 (SRMMLE4). Based on the mean square error matrix criterion, we establish conditions for the superiority of SRMMLE over existing estimators, such as the Stochastic Restricted Maximum Likelihood Estimator (SRMLE), Stochastic Restricted Ridge Maximum Likelihood Estimator (SRRMLE), Stochastic Restricted Logistic Liu Estimator (SRLLE), and Stochastic Restricted Mixed Liu-Type Estimator (SRMLTE). In the simulation study, we determined the scalar mean square error and the K-fold cross-validated balanced accuracy of the estimators. Further, we present an empirical study and a real data application illustrating the superior performance of the proposed estimator. In particular, the SRMMLE4 outperforms others in terms of scalar mean square error and balanced accuracy.
