Abstract

This study aims at comparing the performance of logistic Liu estimators with Maximum Likelihood (ML), Stien and ridge regression estimators using a Monte Carlo simulation, where the mean squared/absolute errors, MSE(β)/MAE(β) mean squared/absolute error between the actual probability $π(x)$ and the estimated probability 􏲯􏱆􏳏􏲯􏱆􏳏$\hat π(x), MSE(π(x))/MAE(π(x))$ are used as performance criteria. An algorithm for simulation steps is included. An application of the effect of quantities of household wastes and its components on the probability of getting a running waste recycling factory is analyzed. Results from both the simulation and the application show that logistic Liu estimators are mostly preferred for correcting mutilcollinearity in logistic regression.

Keywords:

Biased estimators, Liu estimators, logistic regression, multicollinearity, ridge regression estimators, stien estimators,

References

Competing interests

The authors have no competing interests.

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Copyright

The authors have no competing interests.