Estimating Logistic Regression Parameters Using the Adjusted Estimator Method
DOI:
https://doi.org/10.31272/jae.i148.1426Keywords:
Adjusted Estimators, Multi-collinearity, Data Separation, Logistic FunctionAbstract
The problem of separating the binary dependent variable observations that depend on the sample size and the problem of multicollinearity among the explanatory variables are considered two of the most critical issues that arise in the logistic regression model. Treating these problems requires applying effective methods, where the estimation methods of the Double Penalty Maximum Likelihood Estimators (DPMLE) and the Adjusted Estimator Method were adopted in this research. After diagnosing the problem of separation and multicollinearity in the real data, represented by anaemia, which were obtained from the blood disease laboratories at Medicine-City Hospital, several key findings were reached. Chief among them was that the Modified Estimators Method was the best in terms of treating separation and multicollinearity problems. This was reached via dependence on the mean square error (MSE) as a comparison criterion.
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